{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T01:58:07Z","timestamp":1779933487624,"version":"3.53.1"},"reference-count":245,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach. Intell. Res."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a \u201cone-stop-shop\u201d for building a unified understanding of graph counterfactual learning categories and current resources.<\/jats:p>","DOI":"10.1007\/s11633-024-1519-z","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T04:21:23Z","timestamp":1737692483000},"page":"17-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Counterfactual Learning on Graphs: A Survey"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-7640","authenticated-orcid":false,"given":"Zhimeng","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charu","family":"Aggarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3448-4878","authenticated-orcid":false,"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"1519_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01588-5","volume-title":"Graph Representation Learning","author":"W L Hamilton","year":"2020","unstructured":"W. L. Hamilton. Graph Representation Learning, Cham, Switzerland: Springer, 2020. DOI: https:\/\/doi.org\/10.1007\/978-3-031-01588-5."},{"key":"1519_CR2","doi-asserted-by":"publisher","unstructured":"S. Tabassum, F. S. F. Pereira, S. Fernandes, J. Gama. Social network analysis: An overview. WIREs Data Mining and Knowledge Discovery, vol. 8, no. 5, Article number e1256, 2018. DOI: https:\/\/doi.org\/10.1002\/widm.1256.","DOI":"10.1002\/widm.1256"},{"issue":"2","key":"1519_CR3","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1039\/C8SC04228D","volume":"10","author":"C W Coley","year":"2019","unstructured":"C. W. Coley, W. G. Jin, L. Rogers, T. F. Jamison, T. S. Jaakkola, W. H. Green, R. Barzilay, K. F. Jensen. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, vol. 10, no. 2, pp. 370\u2013377, 2019. DOI: https:\/\/doi.org\/10.1039\/C8SC04228D.","journal-title":"Chemical Science"},{"key":"1519_CR4","doi-asserted-by":"publisher","first-page":"9612","DOI":"10.1609\/aaai.v34i05.6508","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"Z Zhang","year":"2020","unstructured":"Z. Zhang, F. Z. Zhuang, H. S. Zhu, Z. P. Shi, H. Xiong, Q. He. Relational graph neural network with hierarchical attention for knowledge graph completion. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, pp. 9612\u20139619, 2020. DOI: https:\/\/doi.org\/10.1609\/aaai.v34i05.6508."},{"key":"1519_CR5","doi-asserted-by":"publisher","unstructured":"S. W. Wu, F. Sun, W. T. Zhang, X. Xie, B. Cui. Graph neural networks in recommender systems: A survey. ACM Computing Surveys, vol. 55, no. 5, Article number 97, 2023. DOI: https:\/\/doi.org\/10.1145\/3535101.","DOI":"10.1145\/3535101"},{"issue":"16","key":"1519_CR6","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1016\/j.physa.2011.12.021","volume":"391","author":"A L Traud","year":"2012","unstructured":"A. L. Traud, P. J. Mucha, M. A. Porter. Social structure of facebook networks. Physica A: Statistical Mechanics and its Applications, vol. 391, no. 16, pp. 4165\u20134180, 2012. DOI: https:\/\/doi.org\/10.1016\/j.physa.2011.12.021.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"1519_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534266","volume-title":"Proceedings of International Joint Conference on Neural Networks","author":"D Numeroso","year":"2021","unstructured":"D. Numeroso, D. Bacciu. MEG: Generating molecular counterfactual explanations for deep graph networks. In Proceedings of International Joint Conference on Neural Networks, Shenzhen, China, 2021. DOI: https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534266."},{"issue":"3","key":"1519_CR8","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","volume":"4","author":"Y Y Wang","year":"2022","unstructured":"Y. Y. Wang, J. R. Wang, Z. L. Cao, A. B. Farimani. Molecular contrastive learning of representations via graph neural networks. Nature Machine Intelligence, vol. 4, no. 3, pp. 279\u2013287, 2022. DOI: https:\/\/doi.org\/10.1038\/s42256-022-00447-x.","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"1519_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z H Wu","year":"2021","unstructured":"Z. H. Wu, S. R. Pan, F. W. Chen, G. D. Long, C. Q. Zhang, P. S. Yu. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4\u201324, 2021. DOI: https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1519_CR10","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1145\/2736277.2741093","volume-title":"Proceedings of the 24th International Conference on World Wide Web","author":"J Tang","year":"2015","unstructured":"J. Tang, M. Qu, M. Z. Wang, M. Zhang, J. Yan, Q. Z. Mei. LINE: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp. 1067\u20131077, 2015. DOI: https:\/\/doi.org\/10.1145\/2736277.2741093."},{"key":"1519_CR11","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"A Grover","year":"2016","unstructured":"A. Grover, J. Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp. 855\u2013864, 2016. DOI: https:\/\/doi.org\/10.1145\/2939672.2939754."},{"key":"1519_CR12","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"T N Kipf","year":"2017","unstructured":"T. N. Kipf, M. Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 2017."},{"key":"1519_CR13","first-page":"5171","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"M H Zhang","year":"2018","unstructured":"M. H. Zhang, Y. X. Chen. Link prediction based on graph neural networks. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 5171\u20135181, 2018."},{"key":"1519_CR14","volume-title":"Overlapping community detection with graph neural networks","author":"O Shchur","year":"2019","unstructured":"O. Shchur, S. G\u00fcnnemann. Overlapping community detection with graph neural networks, [Online], Available: https:\/\/arxiv.org\/abs\/1909.12201, 2019."},{"key":"1519_CR15","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1145\/3534678.3539366","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Y D Sui","year":"2022","unstructured":"Y. D. Sui, X. Wang, J. C. Wu, M. Lin, X. N. He, T. S. Chua. Causal attention for interpretable and generalizable graph classification. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington DC, USA, pp. 1696\u20131705, 2022. DOI: https:\/\/doi.org\/10.1145\/3534678.3539366."},{"key":"1519_CR16","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"K M He","year":"2016","unstructured":"K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770\u2013778, 2016. DOI: https:\/\/doi.org\/10.1109\/CVPR.2016.90."},{"key":"1519_CR17","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.18653\/V1\/N19-1423","volume-title":"Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"J Devlin","year":"2019","unstructured":"J. Devlin, M. W. Chang, K. Lee, K. Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, USA, pp. 4171\u20134186, 2019. DOI: https:\/\/doi.org\/10.18653\/V1\/N19-1423."},{"key":"1519_CR18","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-031-01587-8_7","volume-title":"Introduction to Graph Neural Networks","author":"Z Y Liu","year":"2020","unstructured":"Z. Y. Liu, J. Zhou. Graph attention networks. Introduction to Graph Neural Networks, Z. Y. Liu, J. Zhou, Eds., Cham, Switzerland: Springer, pp. 39\u201341, 2020. DOI: https:\/\/doi.org\/10.1007\/978-3-031-01587-8_7."},{"key":"1519_CR19","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"K Y L Xu","year":"2019","unstructured":"K. Y. L. Xu, W. H. Hu, J. Leskovec, S. Jegelka. How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations, New Orleans, USA, 2019."},{"key":"1519_CR20","first-page":"1725","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"M Chen","year":"2020","unstructured":"M. Chen, Z. W. Wei, Z. F. Huang, B. L. Ding, Y. L. Li. Simple and deep graph convolutional networks. In Proceedings of the 37th International Conference on Machine Learning, pp. 1725\u20131735, 2020."},{"key":"1519_CR21","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"J Gasteiger","year":"2019","unstructured":"J. Gasteiger, A. Bojchevski, S. G\u00fcnnemann. Predict then propagate: Graph neural networks meet personalized pagerank. In Proceedings of the 7th International Conference on Learning Representations, New Orleans, USA, 2019."},{"key":"1519_CR22","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"D Kim","year":"2021","unstructured":"D. Kim, A. Oh. How to find your friendly neighborhood: Graph attention design with self-supervision. In Proceedings of the 9th International Conference on Learning Representations, 2021."},{"key":"1519_CR23","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"L M Pan","year":"2022","unstructured":"L. M. Pan, C. Shi, I. Dokmani\u0107. Neural link prediction with walk pooling. In Proceedings of the 10th International Conference on Learning Representations, 2022."},{"key":"1519_CR24","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"P A Papp","year":"2021","unstructured":"P. A. Papp, K. Martinkus, L. Faber, R. Wattenhofer. DropGNN: Random dropouts increase the expressiveness of graph neural networks. In Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021."},{"issue":"16","key":"1519_CR25","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","volume":"63","author":"Z P Xiong","year":"2020","unstructured":"Z. P. Xiong, D. Y. Wang, X. H. Liu, F. S. Zhong, X. Z. Wan, X. T. Li, Z. J. Li, X. M. Luo, K. X. Chen, H. L. Jiang, M. Y. Zheng. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of Medicinal Chemistry, vol. 63, no. 16, pp. 8749\u20138760, 2020. DOI: https:\/\/doi.org\/10.1021\/acs.jmedchem.9b00959.","journal-title":"Journal of Medicinal Chemistry"},{"issue":"1","key":"1519_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"K. Shu, A. Sliva, S. H. Wang, J. L. Tang, H. Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22\u201336, 2017. DOI: https:\/\/doi.org\/10.1145\/3137597.3137600.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"1519_CR27","volume-title":"FedGNN: Federated graph neural network for privacy-preserving recommendation","author":"C H Wu","year":"2021","unstructured":"C. H. Wu, F. Z. Wu, Y. Cao, Y. F. Huang, X. Xie. FedGNN: Federated graph neural network for privacy-preserving recommendation, [Online], Available: https:\/\/arxiv.org\/abs\/2102.04925, 2021."},{"key":"1519_CR28","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1145\/3437963.3441752","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"E Y Dai","year":"2021","unstructured":"E. Y. Dai, S. H. Wang. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 680\u2013688, 2021. DOI: https:\/\/doi.org\/10.1145\/3437963.3441752."},{"key":"1519_CR29","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1145\/3459637.3482306","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"E Y Dai","year":"2021","unstructured":"E. Y. Dai, S. H. Wang. Towards self-explainable graph neural network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Queensland, Australia, pp. 302\u2013311, 2021. DOI: https:\/\/doi.org\/10.1145\/3459637.3482306."},{"key":"1519_CR30","first-page":"26911","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"T Zhao","year":"2022","unstructured":"T. Zhao, G. Liu, D. Wang, W. Yu, M. Jiang. Learning from counterfactual links for link prediction. In Proceedings of the 39th International Conference on Machine Learning, Baltimore, USA, pp. 26911\u201326926, 2022."},{"key":"1519_CR31","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1145\/3485447.3512173","volume-title":"Proceedings of ACM Web Conference","author":"Y S Dong","year":"2022","unstructured":"Y. S. Dong, N. H. Liu, B. Jalaian, J. D. Li. EDITS: Modeling and mitigating data bias for graph neural networks. In Proceedings of ACM Web Conference, Lyon, France, pp. 1259\u20131269, 2022. DOI: https:\/\/doi.org\/10.1145\/3485447.3512173."},{"key":"1519_CR32","volume-title":"Fairness-aware node representation learning","author":"\u00d6 D D K\u00f6se","year":"2021","unstructured":"\u00d6. D. K\u00f6se, Y. N. Shen. Fairness-aware node representation learning, [Online], Available: https:\/\/arxiv.org\/abs\/2106.05391, 2021."},{"key":"1519_CR33","volume-title":"A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability","author":"E Y Dai","year":"2022","unstructured":"E. Y. Dai, T. X. Zhao, H. S. Zhu, J. J. Xu, Z. M. Guo, H. Liu, J. L. Tang, S. H. Wang. A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability, [Online], Available: https:\/\/arxiv.org\/abs\/2204.08570, 2022."},{"key":"1519_CR34","first-page":"4069","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"M Kusner","year":"2017","unstructured":"M. Kusner, J. R. Loftus, C. Russell, R. Silva. Counterfactual fairness. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. 4069\u20134079, 2017."},{"key":"1519_CR35","volume-title":"Counterfactual explanations and algorithmic resourses for machine learning: A review","author":"S Verma","year":"2020","unstructured":"S. Verma, V. Boonsanong, M. Hoang, K. E. Hines, J. P. Dickerson, C. Shah. Counterfactual explanations and algorithmic resourses for machine learning: A review, [Online], Available: https:\/\/arxiv.org\/abs\/2010.10596, 2020."},{"key":"1519_CR36","first-page":"3976","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"S Pitis","year":"2020","unstructured":"S. Pitis, E. Creager, A. Garg. Counterfactual data augmentation using locally factored dynamics. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 3976\u20133990, 2020."},{"key":"1519_CR37","doi-asserted-by":"publisher","unstructured":"L. Y. Yao, Z. X. Chu, S. Li, Y. L. Li, J. Gao, A. D. Zhang. A survey on causal inference. ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 5, Article number 74, 2021. DOI: https:\/\/doi.org\/10.1145\/3444944.","DOI":"10.1145\/3444944"},{"issue":"1","key":"1519_CR38","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1162\/003465304323023651","volume":"86","author":"G W Imbens","year":"2004","unstructured":"G. W. Imbens. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, vol. 86, no. 1, pp. 4\u201329, 2004. DOI: https:\/\/doi.org\/10.1162\/003465304323023651.","journal-title":"Review of Economics and Statistics"},{"issue":"448","key":"1519_CR39","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1080\/01621459.1999.10473858","volume":"94","author":"R H Dehejia","year":"1999","unstructured":"R. H. Dehejia, S. Wahba. Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American statistical Association, vol. 94, no. 448, pp. 1053\u20131062, 1999. DOI: https:\/\/doi.org\/10.1080\/01621459.1999.10473858.","journal-title":"Journal of the American statistical Association"},{"issue":"9875","key":"1519_CR40","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1016\/S0140-6736(12)62129-1","volume":"381","author":"Cross-Disorder Group of the Psychiatric Genomics Consortium","year":"2013","unstructured":"Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. The Lancet, vol. 381, no. 9875, pp. 1371\u20131379, 2013. DOI: https:\/\/doi.org\/10.1016\/S0140-6736(12)62129-1.","journal-title":"The Lancet"},{"key":"1519_CR41","doi-asserted-by":"publisher","unstructured":"R. C. Guo, L. Cheng, J. D. Li, P. R. Hahn, H. Liu. A survey of learning causality with data: Problems and methods. ACM Computing Surveys, vol. 53, no. 4, Article mumber 75, 2021. DOI: https:\/\/doi.org\/10.1145\/3397269.","DOI":"10.1145\/3397269"},{"key":"1519_CR42","volume-title":"Causal machine learning: A survey and open problems","author":"J Kaddour","year":"2022","unstructured":"J. Kaddour, A. Lynch, Q. Liu, M. J. Kusner, R. Silva. Causal machine learning: A survey and open problems, [Online], Available: https:\/\/arxiv.org\/abs\/2206.15475, 2022."},{"key":"1519_CR43","volume-title":"Causal Inference in Statistics: A Primer","author":"J Pearl","year":"2016","unstructured":"J. Pearl, M. Glymour, N. P. Jewell. Causal Inference in Statistics: A Primer, Chichester, USA: John Wiley & Sons, 2016."},{"key":"1519_CR44","doi-asserted-by":"publisher","unstructured":"N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys, vol. 54, no. 6, Article number 115, 2022. DOI: https:\/\/doi.org\/10.1145\/3457607.","DOI":"10.1145\/3457607"},{"key":"1519_CR45","doi-asserted-by":"publisher","unstructured":"D. V. Carvalho, E. M. Pereira, J. S. Cardoso. Machine learning interpretability: A survey on methods and metrics. Electronics, vol. 8, no. 8, Article number 832, 2019. DOI: https:\/\/doi.org\/10.3390\/electronics8080832.","DOI":"10.3390\/electronics8080832"},{"key":"1519_CR46","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Z Ying","year":"2019","unstructured":"Z. Ying, D. Bourgeois, J. X. You, M. Zitnik, J. Leskovec. GNNExplainer: Generating explanations for graph neural networks. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, 2019."},{"key":"1519_CR47","first-page":"4499","volume-title":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","author":"A Lucic","year":"2022","unstructured":"A. Lucic, M. A. Ter Hoeve, G. Tolomei, M. de Rijke, F. Silvestri. CF-GNNexplainer: Counter fact ual explanations for graph neural networks. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, pp. 4499\u20134511, 2022."},{"key":"1519_CR48","volume-title":"Survey on causal-based machine learning fairness notions","author":"K Makhlouf","year":"2020","unstructured":"K. Makhlouf, S. Zhioua, C. Palamidessi. Survey on causal-based machine learning fairness notions, [Online], Available: https:\/\/arxiv.org\/abs\/2010.09553, 2020."},{"key":"1519_CR49","doi-asserted-by":"publisher","first-page":"11974","DOI":"10.1109\/ACCESS.2021.3051315","volume":"9","author":"I Stepin","year":"2021","unstructured":"I. Stepin, J. M. Alonso, A. Catala, M. Pereira-Fari\u00f1a. A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access, vol. 9, pp. 11974\u201312001, 2021. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2021.3051315.","journal-title":"IEEE Access"},{"key":"1519_CR50","volume-title":"On the computation of counterfactual explanations\u2013A survey","author":"A Artelt","year":"2019","unstructured":"A. Artelt, B. Hammer. On the computation of counterfactual explanations\u2013A survey, [Online], Available: https:\/\/arxiv.org\/abs\/1911.07749, 2019."},{"key":"1519_CR51","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.neucom.2022.04.072","volume":"493","author":"L Oneto","year":"2022","unstructured":"L. Oneto, N. Navarin, B. Biggio, F. Errica, A. Micheli, F. Scarselli, M. Bianchini, L. Demetrio, P. Bongini, A. Tacchella, A. Sperduti. Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing, vol. 493, pp. 217\u2013243, 2022. DOI: https:\/\/doi.org\/10.1016\/J.NEUCOM.2022.04.072.","journal-title":"Neurocomputing"},{"key":"1519_CR52","volume-title":"A survey on graph counterfactual explanations: Definitions, methods, evaluation","author":"M A Prado-Romero","year":"2022","unstructured":"M. A. Prado-Romero, B. Prenkaj, G. Stilo, F. Giannotti. A survey on graph counterfactual explanations: Definitions, methods, evaluation, [Online], Available: https:\/\/arxiv.org\/abs\/2210.12089, 2022."},{"key":"1519_CR53","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1162\/tacl_a_00511","volume":"10","author":"A Feder","year":"2022","unstructured":"A. Feder, K. A. Keith, E. Manzoor, R. Pryzant, D. Sridhar, Z. Wood-Doughty, J. Eisenstein, J. Grimmer, R. Reichart, M. E. Roberts, B. M. Stewart, V. Veitch, D. Y. Yang. Causal inference in natural language processing: Estimation, prediction, interpretation and beyond. Transactions of the Association for Computational Linguistics, vol. 10, pp. 1138\u20131158, 2022. DOI: https:\/\/doi.org\/10.1162\/tacl_a_00511.","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"1519_CR54","doi-asserted-by":"publisher","first-page":"4374","DOI":"10.24963\/ijcai.2021\/598","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"L Cheng","year":"2021","unstructured":"L. Cheng, A. Mosallanezhad, P. Sheth, H. Liu. Causal learning for socially responsible AI. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 4374\u20134381, 2021. DOI: https:\/\/doi.org\/10.24963\/ijcai.2021\/598."},{"issue":"6","key":"1519_CR55","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s11633-022-1362-z","volume":"19","author":"Y Liu","year":"2022","unstructured":"Y. Liu, Y. S. Wei, H. Yan, G. B. Li, L. Lin. Causal reasoning meets visual representation learning: A prospective study. Machine Intelligence Research, vol. 19, no. 6, pp. 485\u2013511, 2022. DOI: https:\/\/doi.org\/10.1007\/S11633-022-1362-z.","journal-title":"Machine Intelligence Research"},{"key":"1519_CR56","doi-asserted-by":"publisher","unstructured":"P. Sanchez, J. P. Voisey, T. Xia, H. I. Watson, A. Q. O\u2019Neil, S. A. Tsaftaris. Causal machine learning for healthcare and precision medicine. Royal Society Open Science, vol. 9, no. 8, Article number 220638, 2022. DOI: https:\/\/doi.org\/10.1098\/rsos.220638.","DOI":"10.1098\/rsos.220638"},{"key":"1519_CR57","doi-asserted-by":"publisher","DOI":"10.59275\/j.melba.2022-4gf2","volume-title":"A review of causality for learning algorithms in medical image analysis","author":"A Vlontzos","year":"2022","unstructured":"A. Vlontzos, D. Rueckert, B. Kainz. A review of causality for learning algorithms in medical image analysis, [Online], Available: https:\/\/arxiv.org\/abs\/2206.05498, 2022."},{"issue":"6","key":"1519_CR58","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1109\/TRPMS.2021.3066428","volume":"5","author":"F L Fan","year":"2021","unstructured":"F. L. Fan, J. J. Xiong, M. Z. Li, G. Wang. On interpretability of artificial neural networks: A survey. IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 6, pp. 741\u2013760, 2021. DOI: https:\/\/doi.org\/10.1109\/trpms.2021.3066428.","journal-title":"IEEE Transactions on Radiation and Plasma Medical Sciences"},{"issue":"5","key":"1519_CR59","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","volume":"66","author":"D B Rubin","year":"1974","unstructured":"D. B. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, vol. 66, no. 5, pp. 688\u2013701, 1974. DOI: https:\/\/doi.org\/10.1037\/h0037350.","journal-title":"Journal of Educational Psychology"},{"issue":"469","key":"1519_CR60","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1198\/016214504000001880","volume":"100","author":"D B Rubin","year":"2005","unstructured":"D. B. Rubin. Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, vol. 100, no. 469, pp. 322\u2013331, 2005. DOI: https:\/\/doi.org\/10.1198\/016214504000001880.","journal-title":"Journal of the American Statistical Association"},{"key":"1519_CR61","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1146\/annurev-clinpsy-040510-143934","volume":"7","author":"E T Bullmore","year":"2011","unstructured":"E. T. Bullmore, D. S. Bassett. Brain graphs: Graphical models of the human brain connectome. Annual Review of Clinical Psychology, vol. 7, pp. 113\u2013140, 2011. DOI: https:\/\/doi.org\/10.1146\/annurev-clinpsy-040510-143934.","journal-title":"Annual Review of Clinical Psychology"},{"key":"1519_CR62","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1145\/3219819.3219890","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"R Ying","year":"2018","unstructured":"R. Ying, R. N. He, K. F. Chen, P. Eksombatchai, W. L. Hamilton, J. Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, pp. 974\u2013983, 2018. DOI: https:\/\/doi.org\/10.1145\/3219819.3219890."},{"key":"1519_CR63","doi-asserted-by":"publisher","first-page":"4438","DOI":"10.1609\/aaai.v32i1.11782","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"M H Zhang","year":"2018","unstructured":"M. H. Zhang, Z. C. Cui, M. Neumann, Y. X. Chen. An end-to-end deep learning architecture for graph classification. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 4438\u20134445, 2018. DOI: https:\/\/doi.org\/10.1609\/aaai.v32i1.11782."},{"key":"1519_CR64","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-1-4419-8462-3_5","volume-title":"Social Network Data Analytics","author":"S Bhagat","year":"2011","unstructured":"S. Bhagat, G. Cormode, S. Muthukrishnan. Node classification in social networks. Social Network Data Analytics, C. C. Aggarwal, Ed., New York, USA: Springer, pp. 115\u2013148, 2011. DOI: https:\/\/doi.org\/10.1007\/978-1-4419-8462-3_5."},{"key":"1519_CR65","first-page":"1201","volume":"11","author":"S V N Vishwanathan","year":"2010","unstructured":"S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, K. M. Borgwardt. Graph kernels. The Journal of Machine Learning Research, vol. 11, pp. 1201\u20131242, 2010.","journal-title":"The Journal of Machine Learning Research"},{"issue":"7","key":"1519_CR66","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"D. Liben-Nowell, J. Kleinberg. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, vol. 58, no. 7, pp. 1019\u20131031, 2007. DOI: https:\/\/doi.org\/10.1002\/ASI.20591.","journal-title":"Journal of the American Society for Information Science and Technology"},{"issue":"2","key":"1519_CR67","doi-asserted-by":"publisher","first-page":"e177","DOI":"10.1093\/bioinformatics\/btl301","volume":"23","author":"N Pr\u017eulj","year":"2007","unstructured":"N. Pr\u017eulj. Biological network comparison using graphlet degree distribution. Bioinformatics, vol. 23, no. 2, pp. e177\u2013e183, 2007. DOI: https:\/\/doi.org\/10.1093\/BIOINFORMATICS\/BTL301.","journal-title":"Bioinformatics"},{"key":"1519_CR68","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1145\/3159652.3159706","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"J Z Qiu","year":"2018","unstructured":"J. Z. Qiu, Y. X. Dong, H. Ma, J. Li, K. S. Wang, J. Tang. Network embedding as matrix factorization: Unifying DeepWalk, LINE, PTE, and node2vec. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, USA, pp. 459\u2013467, 2018. DOI: https:\/\/doi.org\/10.1145\/3159652.3159706."},{"key":"1519_CR69","volume-title":"Proceedings of the 6th International Conference on Learning Representations","author":"P Veli\u010dkovi\u0107","year":"2018","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2, Y. Bengio. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, Vancouver, Canada, 2018."},{"key":"1519_CR70","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.1145\/3447548.3467451","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"T Xiao","year":"2021","unstructured":"T. Xiao, Z. Y. Chen, D. L. Wang, S. H. Wang. Learning how to propagate messages in graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1894\u20131903, 2021. DOI: https:\/\/doi.org\/10.1145\/3447548.3467451."},{"key":"1519_CR71","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/ICDM54844.2022.00165","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"J J Xu","year":"2022","unstructured":"J. J. Xu, E. Y. Dai, X. Zhang, S. H. Wang. HP-GMN: Graph memory networks for heterophilous graphs. In Proceedings of IEEE International Conference on Data Mining, Orlando, USA, pp. 1263\u20131268, 2022. DOI: https:\/\/doi.org\/10.1109\/ICDM54844.2022.00165."},{"key":"1519_CR72","volume-title":"Proceedings of the 1st Learning on Graphs Conference","author":"T X Zhao","year":"2022","unstructured":"T. X. Zhao, D. S. Luo, X. Zhang, S. H. Wang. Topoimb: Toward topology-level imbalance in learning from graphs. In Proceedings of the 1st Learning on Graphs Conference, Article number 37, 2022."},{"key":"1519_CR73","volume-title":"Rethinking graph backdoor attacks: A distribution-preserving perspective","author":"Z W Zhang","year":"2024","unstructured":"Z. W. Zhang, M. H. Lin, E. Y. Dai, S. H. Wang. Rethinking graph backdoor attacks: A distribution-preserving perspective, [Online], Available: https:\/\/arxiv.org\/abs\/2405.10757, 2024."},{"key":"1519_CR74","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"M H Lin","year":"2023","unstructured":"M. H. Lin, T. Xiao, E. Y. Dai, X. Zhang, S. H. Wang. Certifiably robust graph contrastive learning. In Proceedings of the 37th International Conference on Neural Information Processing Systems, New Orleans, USA, 2023."},{"key":"1519_CR75","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1145\/3447548.3467364","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"E Y Dai","year":"2021","unstructured":"E. Y. Dai, C. Aggarwal, S. H. Wang. NRGNN: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, pp. 227\u2013236, 2021. DOI: https:\/\/doi.org\/10.1145\/3447548.3467364."},{"key":"1519_CR76","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1145\/3437963.3441720","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"T X Zhao","year":"2021","unstructured":"T. X. Zhao, X. Zhang, S. H. Wang. GraphSMOTE: Imbalanced node classification on graphs with graph neural networks. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 833\u2013841, 2021. DOI: https:\/\/doi.org\/10.1145\/3437963.3441720."},{"key":"1519_CR77","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1145\/3488560.3498408","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining","author":"E Y Dai","year":"2022","unstructured":"E. Y. Dai, W. Jin, H. Liu, S. H. Wang. Towards robust graph neural networks for noisy graphs with sparse labels. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Arizona, USA, pp. 181\u2013191, 2022. DOI: https:\/\/doi.org\/10.1145\/3488560.3498408."},{"key":"1519_CR78","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1145\/3616855.3635793","volume-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","author":"F L Wang","year":"2024","unstructured":"F. L. Wang, T. X. Zhao, S. H. Wang. Distribution consistency based self-training for graph neural networks with sparse labels. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Merida, Mexico, pp. 712\u2013720, 2024. DOI: https:\/\/doi.org\/10.1145\/3616855.3635793."},{"key":"1519_CR79","doi-asserted-by":"publisher","unstructured":"N. M. Nasrabadi. Pattern recognition and machine learning. Journal of Electronic Imaging, vol. 16, no. 4, Article number 049901, 2007. DOI: https:\/\/doi.org\/10.1117\/1.2819119.","DOI":"10.1117\/1.2819119"},{"key":"1519_CR80","first-page":"6781","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"E Z Liu","year":"2021","unstructured":"E. Z. Liu, B. Haghgoo, A. S. Chen, A. Raghunathan, P. W. Koh, S. Sagawa, P. Liang, C. Finn. Just train twice: Improving group robustness without training group information. In Proceedings of the 38th International Conference on Machine Learning, pp. 6781\u20136792, 2021."},{"key":"1519_CR81","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"S Sagawa","year":"2020","unstructured":"S. Sagawa, P. W. Koh, T. B. Hashimoto, P. Liang. Distributionally robust neural networks. In Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020."},{"key":"1519_CR82","volume-title":"Invariant risk minimization","author":"M Arjovsky","year":"2019","unstructured":"M. Arjovsky, L. Bottou, I. Gulrajani, D. Lopez-Paz. Invariant risk minimization, [Online], Available: https:\/\/arxiv.org\/abs\/1907.02893, 2019."},{"key":"1519_CR83","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"J Mitrovic","year":"2021","unstructured":"J. Mitrovic, B. McWilliams, J. C. Walker, L. H. Buesing, C. Blundell. Representation learning via invariant causal mechanisms. In Proceedings of the 9th International Conference on Learning Representations, 2021."},{"key":"1519_CR84","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1214\/09-SS057","volume":"3","author":"J Pearl","year":"2009","unstructured":"J. Pearl. Causal inference in statistics: An overview. Statistics Surveys, vol. 3, pp. 96\u2013146, 2009. DOI: https:\/\/doi.org\/10.1214\/09-SS057.","journal-title":"Statistics Surveys"},{"key":"1519_CR85","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3058954","volume-title":"Towards causal representation learning","author":"B Sch\u00f6lkopf","year":"2021","unstructured":"B. Sch\u00f6lkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio. Towards causal representation learning, [Online], Available: https:\/\/arxiv.org\/abs\/2102.11107, 2021."},{"key":"1519_CR86","doi-asserted-by":"publisher","first-page":"4627","DOI":"10.24963\/ijcai.2021\/628","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"J D Wang","year":"2021","unstructured":"J. D. Wang, C. L. Lan, C. Liu, Y. D. Ouyang, T. Qin. Generalizing to unseen domains: A survey on domain generalization. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 4627\u20134635, 2021. DOI: https:\/\/doi.org\/10.24963\/ijcai.2021\/628."},{"key":"1519_CR87","volume-title":"From statistical to causal learning","author":"B Scholkopf","year":"2022","unstructured":"B. Scholkopf, J. von K\u00fcgelgen. From statistical to causal learning, [Online], Available: https:\/\/arxiv.org\/abs\/2204.00607, 2022."},{"issue":"16","key":"1519_CR88","doi-asserted-by":"publisher","first-page":"3248","DOI":"10.1080\/00949655.2018.1505197","volume":"88","author":"R Shanmugam","year":"2018","unstructured":"R. Shanmugam. Elements of causal inference: Foundations and learning algorithms. Journal of Statistical Computation and Simulation, vol. 88, no. 16, pp. 3248\u20133248, 2018. DOI: https:\/\/doi.org\/10.1080\/00949655.2018.1505197.","journal-title":"Journal of Statistical Computation and Simulation"},{"key":"1519_CR89","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1145\/3488560.3498391","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining","author":"J Ma","year":"2022","unstructured":"J. Ma, R. C. Guo, M. T. Wan, L. Q. Yang, A. D. Zhang, J. D. Li. Learning fair node representations with graph counterfactual fairness. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Arizona, USA, pp. 695\u2013703, 2022. DOI: https:\/\/doi.org\/10.1145\/3488560.3498391."},{"key":"1519_CR90","volume-title":"Fair lending needs explainable models for responsible recommendation","author":"J H Chen","year":"2018","unstructured":"J. H. Chen. Fair lending needs explainable models for responsible recommendation, [Online], Available: https:\/\/arxiv.org\/abs\/1809.04684, 2018."},{"issue":"10","key":"1519_CR91","doi-asserted-by":"publisher","first-page":"10583","DOI":"10.1109\/TKDE.2023.3265598","volume":"35","author":"Y S Dong","year":"2023","unstructured":"Y. S. Dong, J. Ma, S. Wang, C. Chen, J. D. Li. Fairness in graph mining: A survey. IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 10583\u201310602, 2023. DOI: https:\/\/doi.org\/10.1109\/TKDE.2023.3265598.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR92","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1007\/978-3-030-72357-6","volume-title":"Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence","author":"C Agarwal","year":"2021","unstructured":"C. Agarwal, H. Lakkaraju, M. Zitnik. Towards a unified framework for fair and stable graph representation learning. In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, pp. 2114\u20132124, 2021."},{"issue":"7","key":"1519_CR93","doi-asserted-by":"publisher","first-page":"7103","DOI":"10.1109\/TKDE.2022.3197554","volume":"35","author":"E Y Dai","year":"2023","unstructured":"E. Y. Dai, S. H. Wang. Learning fair graph neural networks with limited and private sensitive attribute information. IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 7103\u20137117, 2023. DOI: https:\/\/doi.org\/10.1109\/TKDE.2022.3197554.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR94","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/ICDM51629.2021.00194","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"X Zhang","year":"2021","unstructured":"X. Zhang, L. Zhang, B. Jin, X. J. Lu. A multi-view confidence-calibrated framework for fair and stable graph representation learning. In Proceedings of IEEE International Conference on Data Mining, Auckland, New Zealand, pp. 1493\u20131498, 2021. DOI: https:\/\/doi.org\/10.1109\/ICDM51629.2021.00194."},{"key":"1519_CR95","doi-asserted-by":"publisher","unstructured":"H. S. Zhu, E. Y. Dai, H. Liu, S. H. Wang. Learning fair models without sensitive attributes: A generative approach. Neurocomputing, vol. 561, Article number 126841, 2023. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2023.126841.","DOI":"10.1016\/j.neucom.2023.126841"},{"key":"1519_CR96","first-page":"4721","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"A Jalal","year":"2021","unstructured":"A. Jalal, S. Karmalkar, J. Hoffmann, A. Dimakis, E. Price. Fairness for image generation with uncertain sensitive attributes. In Proceedings of the 38th International Conference on Machine Learning, pp. 4721\u20134732, 2021."},{"key":"1519_CR97","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1145\/3306618.3317950","volume-title":"Proceedings of AAAI\/ACM Conference on AI, Ethics, and Society","author":"S Garg","year":"2019","unstructured":"S. Garg, V. Perot, N. Limtiaco, A. Taly, E. H. Chi, A. Beutel. Counterfactual fairness in text classification through robustness. In Proceedings of AAAI\/ACM Conference on AI, Ethics, and Society, Honolulu, USA, pp. 219\u2013226, 2019. DOI: https:\/\/doi.org\/10.1145\/3306618.3317950."},{"key":"1519_CR98","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1145\/3341302.3342077","volume-title":"Proceedings of ACM Special Interest Group on Data Communication","author":"V Nathan","year":"2019","unstructured":"V. Nathan, V. Sivaraman, R. Addanki, M. Khani, P. Goyal, M. Alizadeh. End-to-end transport for video QoE fairness. In Proceedings of ACM Special Interest Group on Data Communication, Beijing, China, pp. 408\u2013423, 2019. DOI: https:\/\/doi.org\/10.1145\/3341302.3342077."},{"issue":"4175","key":"1519_CR99","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1126\/science.187.4175.398","volume":"187","author":"P J Bickel","year":"1975","unstructured":"P. J. Bickel, E. A. Hammel, J. W. O\u2019Connell. Sex bias in graduate admissions: Data from Berkeley: Measuring bias is harder than is usually assumed, and the evidence is sometimes contrary to expectation. Science, vol. 187, no. 4175, pp. 398\u2013404, 1975. DOI: https:\/\/doi.org\/10.1126\/science.187.4175.398.","journal-title":"Science"},{"key":"1519_CR100","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1609\/AAAI.V34I01.5429","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","author":"F Masrour","year":"2020","unstructured":"F. Masrour, T. Wilson, H. Yan, P. N. Tan, A. Esfahanian. Bursting the filter bubble: Fairness-aware network link prediction. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, pp. 841\u2013848, 2020. DOI: https:\/\/doi.org\/10.1609\/AAAI.V34I01.5429."},{"key":"1519_CR101","first-page":"715","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"A Bose","year":"2019","unstructured":"A. Bose, W. Hamilton. Compositional fairness constraints for graph embeddings. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 715\u2013724, 2019."},{"key":"1519_CR102","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"P Z Li","year":"2021","unstructured":"P. Z. Li, Y. F. Wang, H. Zhao, P. Y. Hong, H. F. Liu. On dyadic fairness: Exploring and mitigating bias in graph connections. In Proceedings of the 9th International Conference on Learning Representations, 2021."},{"key":"1519_CR103","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1145\/3485447.3512189","volume-title":"Proceedings of ACM Web Conference","author":"N Wang","year":"2022","unstructured":"N. Wang, L. Lin, J. D. Li, H. N. Wang. Unbiased graph embedding with biased graph observations. In Proceedings of ACM Web Conference, Lyon, France, pp. 1423\u20131433, 2022. DOI: https:\/\/doi.org\/10.1145\/3485447.3512189."},{"key":"1519_CR104","doi-asserted-by":"publisher","first-page":"11963","DOI":"10.1609\/AAAI.V36I11.21454","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","author":"A Khajehnejad","year":"2022","unstructured":"A. Khajehnejad, M. Khajehnejad, M. Babaei, K. P. Gummadi, A. Weller, B. Mirzasoleiman. CrossWalk: Fairness-enhanced node representation learning. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, pp. 11963\u201311970, 2022. DOI: https:\/\/doi.org\/10.1609\/AAAI.V36I11.21454."},{"key":"1519_CR105","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.24963\/IJCAI.2019\/456","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"T Rahman","year":"2019","unstructured":"T. Rahman, B. Surma, M. Backes, Y. Zhang. Fairwalk: Towards fair graph embedding. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, pp. 3289\u20133295, 2019. DOI: https:\/\/doi.org\/10.24963\/IJCAI.2019\/456."},{"key":"1519_CR106","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1145\/3583780.3615092","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","author":"Z M Guo","year":"2023","unstructured":"Z. M. Guo, J. L. Li, T. Xiao, Y. Ma, S. H. Wang. Towards fair graph neural networks via graph counterfactual. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, UK, pp. 669\u2013678, 2023. DOI: https:\/\/doi.org\/10.1145\/3583780.3615092."},{"key":"1519_CR107","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"H Y Ling","year":"2023","unstructured":"H. Y. Ling, Z. M. Jiang, Y. Z. Luo, S. W. Ji, N. Zou. Learning fair graph representations via automated data augmentations. In Proceedings of the 11th International Conference on Learning Representations, Kigali, Rwanda, 2023."},{"issue":"2","key":"1519_CR108","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/3575637.3575646","volume":"24","author":"K Z Ding","year":"2022","unstructured":"K. Z. Ding, Z. Xu, H. H. Tong, H. Liu. Data augmentation for deep graph learning: A survey. ACM SIGKDD Explorations Newsletter, vol. 24, no. 2, pp. 61\u201377, 2022. DOI: https:\/\/doi.org\/10.1145\/3575637.3575646.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"1519_CR109","volume-title":"FMP: Toward fair graph message passing against topology bias","author":"Z M Jiang","year":"2022","unstructured":"Z. M. Jiang, X. T. Han, C. Fan, Z. R. Liu, N. Zou, A. Mostafavi, X. Hu. FMP: Toward fair graph message passing against topology bias, [Online], Available: https:\/\/arxiv.org\/abs\/2202.04187, 2022."},{"key":"1519_CR110","first-page":"8969","volume-title":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","author":"C Agarwal","year":"2022","unstructured":"C. Agarwal, M. Zitnik, H. Lakkaraju. Probing GNN explainers: A rigorous theoretical and empirical analysis of GNN explanation methods. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, pp. 8969\u20138996, 2022."},{"key":"1519_CR111","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1109\/ICDM58522.2023.00073","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"Z C Wang","year":"2023","unstructured":"Z. C. Wang, G. Narasimhan, X. Yao, W. B. Zhang. Mitigating multisource biases in graph neural networks via real counterfactual samples. In Proceedings of IEEE International Conference on Data Mining, Shanghai, China, pp. 638\u2013647, 2023. DOI: https:\/\/doi.org\/10.1109\/ICDM58522.2023.00073."},{"key":"1519_CR112","doi-asserted-by":"publisher","first-page":"6617","DOI":"10.1007\/s10115-024-02161-z","volume":"66","author":"Z C Wang","year":"2024","unstructured":"Z. C. Wang, M. K. Qiu, M. Chen, M. B. Salem, X. Yao, W. B. Zhang. Toward fair graph neural networks via real counterfactual samples. Knowledge and Information Systems, vol. 66, pp. 6617\u20136641, 2024. DOI: https:\/\/doi.org\/10.1007\/s10115-024-02161-z.","journal-title":"Knowledge and Information Systems"},{"key":"1519_CR113","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1145\/3442381.3450086","volume-title":"Proceedings of Web Conference","author":"Y Zhang","year":"2021","unstructured":"Y. Zhang, D. Z. Cheng, T. S. Yao, X. Y. Yi, L. C. Hong, E. H. Chi. A model of two tales: Dual transfer learning framework for improved long-tail item recommendation. In Proceedings of Web Conference, Ljubljana, Slovenia, pp. 2220\u20132231, 2021. DOI: https:\/\/doi.org\/10.1145\/3442381.3450086."},{"key":"1519_CR114","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1145\/2090236.2090255","volume-title":"Proceedings of the 3rd Innovations in Theoretical Computer Science","author":"C Dwork","year":"2012","unstructured":"C. Dwork, M. Hardt, T. Pitassi, O. Reingold, R. Zemel. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science, Cambridge, USA, pp. 214\u2013226, 2012. DOI: https:\/\/doi.org\/10.1145\/2090236.2090255."},{"key":"1519_CR115","first-page":"3323","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","author":"M Hardt","year":"2016","unstructured":"M. Hardt, E. Price, N. Srebro. Equality of opportunity in supervised learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 3323\u20133331, 2016."},{"key":"1519_CR116","volume-title":"Proceedings of International Scientific Conference & International Workshop Present Day Trends of Innovations","author":"L Takac","year":"2012","unstructured":"L. Takac, M. Zabovsky. Data analysis in public social networks. In Proceedings of International Scientific Conference & International Workshop Present Day Trends of Innovations, Lomza, Poland, 2012."},{"issue":"2","key":"1519_CR117","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","volume":"36","author":"I C Yeh","year":"2009","unstructured":"I. C. Yeh, C. H. Lien. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, vol. 36, no. 2, pp. 2473\u20132480, 2009. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2007.12.020.","journal-title":"Expert Systems with Applications"},{"key":"1519_CR118","volume-title":"UCI machine learning repository","author":"A Asuncion","year":"2007","unstructured":"A. Asuncion, D. J. Newman. UCI machine learning repository, Irvine University of California, Irvine, USA, 2007."},{"issue":"3","key":"1519_CR119","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1080\/15377938.2014.984045","volume":"13","author":"K L Jordan","year":"2015","unstructured":"K. L. Jordan, T. L. Freiburger. The effect of race\/ethnicity on sentencing: Examining sentence type, jail length, and prison length. Journal of Ethnicity in Criminal Justice, vol. 13, no. 3, pp. 179\u2013196, 2015. DOI: https:\/\/doi.org\/10.1080\/15377938.2014.984045.","journal-title":"Journal of Ethnicity in Criminal Justice"},{"key":"1519_CR120","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1146\/annurev-physchem-042018-052331","volume":"71","author":"F No\u00e9","year":"2020","unstructured":"F. No\u00e9, A. Tkatchenko, K. R. M\u00fcller, C. Clementi. Machine learning for molecular simulation. Annual Review of Physical Chemistry, vol. 71, pp. 361\u2013390, 2020. DOI: https:\/\/doi.org\/10.1146\/annurev-physchem-042018-052331.","journal-title":"Annual Review of Physical Chemistry"},{"key":"1519_CR121","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"A. Barredo Arrieta, N. D\u00edaz-Rodr\u00edguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, F. Herrera. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, vol. 58, pp. 82\u2013115, 2020. DOI: https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012.","journal-title":"Information Fusion"},{"key":"1519_CR122","series-title":"Master dissertation","volume-title":"Interpretable Machine Learning","author":"T Martin","year":"2019","unstructured":"T. Martin. Interpretable Machine Learning, Master dissertation, University of Cambridge, UK, 2019."},{"issue":"2","key":"1519_CR123","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/JPROC.2024.3369017","volume":"112","author":"H Zhang","year":"2024","unstructured":"H. Zhang, B. Wu, X. L. Yuan, S. R. Pan, H. H. Tong, J. Pei. Trustworthy graph neural networks: Aspects, methods, and trends. Proceedings of the IEEE, vol. 112, no. 2, pp. 97\u2013139, 2024. DOI: https:\/\/doi.org\/10.1109\/JPROC.2024.3369017.","journal-title":"Proceedings of the IEEE"},{"key":"1519_CR124","doi-asserted-by":"publisher","unstructured":"T. X. Zhao, D. S. Luo, X. Zhang, S. H. Wang. Faithful and consistent graph neural network explanations with rationale alignment. ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 5, Article number 92, 2023. DOI: https:\/\/doi.org\/10.1145\/3616542.","DOI":"10.1145\/3616542"},{"key":"1519_CR125","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1145\/3539597.3570421","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"T X Zhao","year":"2023","unstructured":"T. X. Zhao, D. S. Luo, X. Zhang, S. H. Wang. Towards faithful and consistent explanations for graph neural networks. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, Singapore, pp. 634\u2013642, 2023. DOI: https:\/\/doi.org\/10.1145\/3539597.3570421."},{"key":"1519_CR126","doi-asserted-by":"publisher","unstructured":"H. X. Cai, H. M. Zhang, D. C. Zhao, J. X. Wu, L. Wang. FP-GNN: A versatile deep learning architecture for enhanced molecular property prediction. Briefings in Bioinformatics, vol. 23, no. 6, Artile number bbac408, 2022. DOI: https:\/\/doi.org\/10.1093\/BIB\/BBAC408.","DOI":"10.1093\/BIB\/BBAC408"},{"issue":"5","key":"1519_CR127","doi-asserted-by":"publisher","first-page":"5782","DOI":"10.1109\/TPAMI.2022.3204236","volume":"45","author":"H Yuan","year":"2023","unstructured":"H. Yuan, H. Y. Yu, S. R. Gui, S. W. Ji. Explainability in graph neural networks: A taxonomic survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 5, pp. 5782\u20135799, 2023. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2022.3204236.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1519_CR128","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"D S Luo","year":"2020","unstructured":"D. S. Luo, W. Cheng, D. K. Xu, W. C. Yu, B. Zong, H. F. Chen, X. Zhang. Parameterized explainer for graph neural network. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020."},{"key":"1519_CR129","volume-title":"Proceedings of 35th International Conference on Neural Information Processing Systems","author":"M Bajaj","year":"2021","unstructured":"M. Bajaj, L. Y. Chu, Z. Y. Xue, J. Pei, L. J. Wang, P. C. H. Lam, Y. Zhang. Robust counterfactual explanations on graph neural networks. In Proceedings of 35th International Conference on Neural Information Processing Systems, 2021."},{"key":"1519_CR130","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1145\/3485447.3511948","volume-title":"Proceedings of ACM Web Conference","author":"J T Tan","year":"2022","unstructured":"J. T. Tan, S. J. Geng, Z. H. Fu, Y. Q. Ge, S. Y. Xu, Y. Q. Li, Y. F. Zhang. Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In Proceedings of ACM Web Conference, Lyon, France, pp. 1018\u20131027, 2022. DOI: https:\/\/doi.org\/10.1145\/3485447.3511948."},{"key":"1519_CR131","volume-title":"Preserve, promote, or attack? GNN explanation via topology perturbation","author":"Y Sun","year":"2021","unstructured":"Y. Sun, A. Valente, S. J. Liu, D. K. Wang. Preserve, promote, or attack? GNN explanation via topology perturbation, [Online], Available: https:\/\/arxiv.org\/abs\/2103.13944, 2021."},{"key":"1519_CR132","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1145\/3447548.3467154","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"C Abrate","year":"2021","unstructured":"C. Abrate, F. Bonchi. Counterfactual graphs for explainable classification of brain networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, pp. 2495\u20132504, 2021. DOI: https:\/\/doi.org\/10.1145\/3447548.3467154."},{"key":"1519_CR133","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"J Ma","year":"2022","unstructured":"J. Ma, R. C. Guo, S. Mishra, A. D. Zhang, J. Li. CLEAR: Generative counterfactual explanations on graphs. In Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, USA, 2022."},{"key":"1519_CR134","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1145\/3539597.3570376","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Z X Huang","year":"2023","unstructured":"Z. X. Huang, M. Kosan, S. Medya, S. Ranu, A. Singh. Global counterfactual explainer for graph neural networks. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, Singapore, pp. 141\u2013149, 2023. DOI: https:\/\/doi.org\/10.1145\/3539597.3570376."},{"key":"1519_CR135","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1109\/ICDM51629.2021.00052","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"Y F Liu","year":"2021","unstructured":"Y. F. Liu, C. Chen, Y. Z. Liu, X. Zhang, S. H. Xie. Multi-objective explanations of GNN predictions. In Proceedings of IEEE International Conference on Data Mining, Auckland, New Zealand, pp. 409\u2013418, 2021. DOI: https:\/\/doi.org\/10.1109\/ICDM51629.2021.00052."},{"key":"1519_CR136","volume-title":"On the probability of necessity and sufficiency of explaining graph neural networks: A lower bound optimization approach","author":"R C Cai","year":"2022","unstructured":"R. C. Cai, Y. X. Zhu, X. X. Chen, Y. Fang, M. Wu, J. Qiao, Z. F. Hao. On the probability of necessity and sufficiency of explaining graph neural networks: A lower bound optimization approach, [Online], Available: https:\/\/arxiv.org\/abs\/2212.07056, 2022."},{"key":"1519_CR137","series-title":"Bachelor\u2019s dissertation","volume-title":"Flow-based Counterfactuals for Interpretable Graph Node Classification","author":"L Ohly","year":"2022","unstructured":"L. Ohly. Flow-based Counterfactuals for Interpretable Graph Node Classification, Bachelor\u2019s dissertation, Freien University, Berlin, Germany, 2022."},{"key":"1519_CR138","first-page":"88","volume-title":"Proceedings of the 3rd Italian Workshop on Explainable Artificial Intelligence Co-Located with 21th International Conference of the Italian Association for Artificial Intelligence","author":"M A Prado-Romero","year":"2022","unstructured":"M. A. Prado-Romero, B. Prenkaj, G. Stilo, A. Celi, E. Estevanell-Valladares, D. A. Vald\u00e9s-P\u00e9rez. Ensemble approaches for graph counterfactual explanations. In Proceedings of the 3rd Italian Workshop on Explainable Artificial Intelligence Co-Located with 21th International Conference of the Italian Association for Artificial Intelligence, Udine, Italy, pp. 88\u201397, 2022."},{"key":"1519_CR139","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1145\/3394486.3403085","volume-title":"Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"H Yuan","year":"2020","unstructured":"H. Yuan, J. L. Tang, X. Hu, S. W. Ji. XGNN: Towards model-level explanations of graph neural networks. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 430\u2013438, 2020. DOI: https:\/\/doi.org\/10.1145\/3394486.3403085."},{"key":"1519_CR140","volume-title":"Explainability techniques for graph convolutional networks","author":"F Baldassarre","year":"2019","unstructured":"F. Baldassarre, H. Azizpour. Explainability techniques for graph convolutional networks, [Online], Available: https:\/\/arxiv.org\/abs\/1905.13686, 2019."},{"issue":"7","key":"1519_CR141","doi-asserted-by":"publisher","first-page":"6968","DOI":"10.1109\/TKDE.2022.3187455","volume":"35","author":"Q Huang","year":"2023","unstructured":"Q. Huang, M. Yamada, Y. Tian, D. Singh, Y. Chang. GraphLIME: Local interpretable model explanations for graph neural networks. IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 7, pp. 6968\u20136972, 2023. DOI: https:\/\/doi.org\/10.1109\/TKDE.2022.3187455.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR142","first-page":"15524","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"S Q Miao","year":"2022","unstructured":"S. Q. Miao, M. Liu, P. Li. Interpretable and generalizable graph learning via stochastic attention mechanism. In Proceedings of the 39th International Conference on Machine Learning, Baltimore, USA, pp. 15524\u201315543, 2022."},{"key":"1519_CR143","volume-title":"Towards prototype-based self-explainable graph neural network","author":"E Y Dai","year":"2022","unstructured":"E. Y. Dai, S. H. Wang. Towards prototype-based self-explainable graph neural network, [Online], Available: https:\/\/arxiv.org\/abs\/2210.01974, 2022."},{"key":"1519_CR144","first-page":"6666","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"W Y Lin","year":"2021","unstructured":"W. Y. Lin, H. Lan, B. C. Li. Generative causal explanations for graph neural networks. In Proceedings of the 38th International Conference on Machine Learning, pp. 6666\u20136679, 2021."},{"key":"1519_CR145","volume-title":"Towards realistic individual recourse and actionable explanations in black-box decision making systems","author":"S Joshi","year":"2019","unstructured":"S. Joshi, O. Koyejo, W. Vijitbenjaronk, B. Kim, J. Ghosh. Towards realistic individual recourse and actionable explanations in black-box decision making systems, [Online], Available: https:\/\/arxiv.org\/abs\/1907.09615, 2019."},{"key":"1519_CR146","volume-title":"GREh]A unified framework for graph counterfactual explanation evaluation","author":"M A Prado-Romero","year":"2022","unstructured":"M. A. Prado-Romero, G. Stilo. GREh]A unified framework for graph counterfactual explanation evaluation, [Online], Available: https:\/\/arxiv.org\/abs\/2206.02957, 2022."},{"key":"1519_CR147","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1145\/3589334.3645419","volume-title":"Proceedings of ACM on Web Conference","author":"C Chhablani","year":"2024","unstructured":"C. Chhablani, S. Jain, A. Channesh, I. A. Kash, S. Medya. Game-theoretic counterfactual explanation for graph neural networks. In Proceedings of ACM on Web Conference, Singapore, pp. 503\u2013514, 2024. DOI: https:\/\/doi.org\/10.1145\/3589334.3645419."},{"key":"1519_CR148","volume-title":"Proceedings of the 32nd British Machine Vision Conference","author":"F Hvilsh\u00f8j","year":"2021","unstructured":"F. Hvilsh\u00f8j, A. Iosifidis, I. Assent. ECINN: Efficient counterfactuals from invertible neural networks. In Proceedings of the 32nd British Machine Vision Conference, Article number 43, 2021."},{"issue":"2","key":"1519_CR149","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1287\/moor.4.2.99","volume":"4","author":"P Dubey","year":"1979","unstructured":"P. Dubey, L. S. Shapley. Mathematical properties of the Banzhaf power index. Mathematics of Operations Research, vol. 4, no. 2, pp. 99\u2013131, 1979. DOI: https:\/\/doi.org\/10.1287\/moor.4.2.99.","journal-title":"Mathematics of Operations Research"},{"issue":"2","key":"1519_CR150","first-page":"317","volume":"19","author":"J F Banzhaf III","year":"1965","unstructured":"J. F. BanzhafIII. Weighted voting Doesn\u2019t work: A mathematical analysis. Rutgers Law Review, vol. 19, no. 2, pp. 317\u2013343, 1965.","journal-title":"Rutgers Law Review"},{"key":"1519_CR151","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114","volume-title":"Proceedings of the 2nd International Conference on Learning Representations","author":"D P Kingma","year":"2014","unstructured":"D. P. Kingma, M. Welling. Auto-encoding variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations, Banff, Canada, 2014. DOI: https:\/\/doi.org\/10.48550\/arXiv.1312.6114."},{"issue":"2","key":"1519_CR152","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1021\/jm00106a046","volume":"34","author":"A K Debnath","year":"1991","unstructured":"A. K. Debnath, R. L. Lopez De Compadre, G. Debnath, A. J. Shusterman, C. Hansch. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry, vol. 34, no. 2, pp. 786\u2013797, 1991. DOI: https:\/\/doi.org\/10.1021\/jm00106a046.","journal-title":"Journal of Medicinal Chemistry"},{"issue":"3","key":"1519_CR153","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10115-007-0103-5","volume":"14","author":"N Wale","year":"2008","unstructured":"N. Wale, I. A. Watson, G. Karypis. Comparison of descriptor spaces for chemical compound retrieval and classification. Knowledge and Information Systems, vol. 14, no. 3, pp. 347\u2013375, 2008. DOI: https:\/\/doi.org\/10.1007\/s10115-007-0103-5.","journal-title":"Knowledge and Information Systems"},{"key":"1519_CR154","volume-title":"Benchmark data sets for graph kernels","author":"K Kersting","year":"2016","unstructured":"K. Kersting, N. M. Kriege, C. Morris, P. Mutzel, M. Neumann. Benchmark data sets for graph kernels, [Online], Available: http:\/\/graphkernels.cs.tu-dortmund.de, 2016."},{"key":"1519_CR155","doi-asserted-by":"publisher","unstructured":"C. Craddock, Y. Benhajali, C. Chu, F. Chouinard, A. Evans, A. Jakab, B. S. Khundrakpam, J. D. Lewis, Q. Y. Li, M. Milham, C. G. Yan, P. Bellec. The neuro bureau preprocessing initiative: Open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, vol. 7, Article number 27, 2013. DOI: https:\/\/doi.org\/10.3389\/conf.fninf.2013.09.00041.","DOI":"10.3389\/conf.fninf.2013.09.00041"},{"key":"1519_CR156","doi-asserted-by":"publisher","unstructured":"J. A. Brown, J. D. Rudie, A. Bandrowski, J. D. Van Horn, S. Y. Bookheimer. The UCLA multimodal connectivity database: A web-based platform for brain connectivity matrix sharing and analysis. Frontiers in Neuroinformatics, vol. 6, Article number 28, 2012. DOI: https:\/\/doi.org\/10.3389\/fninf.2012.00028.","DOI":"10.3389\/fninf.2012.00028"},{"key":"1519_CR157","doi-asserted-by":"publisher","unstructured":"A. Kumar, S. S. Singh, K. Singh, B. Biswas. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications, vol. 553, Article number 124289, 2020. DOI: https:\/\/doi.org\/10.1016\/j.physa.2020.124289.","DOI":"10.1016\/j.physa.2020.124289"},{"key":"1519_CR158","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1145\/3404835.3462962","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"W J Wang","year":"2021","unstructured":"W. J. Wang, F. L. Feng, X. N. He, H. W. Zhang, T. S. Chua. Clicks can be cheating: Counterfactual recommendation for mitigating clickbait issue. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1288\u20131297, 2021. DOI: https:\/\/doi.org\/10.1145\/3404835.3462962."},{"key":"1519_CR159","volume-title":"GREASE: Generate factual and counterfactual explanations for GNN-based recommendations","author":"Z H Chen","year":"2022","unstructured":"Z. H. Chen, F. Silvestri, J. Wang, Y. F. Zhang, Z. H. Huang, H. Ahn, G. Tolomei. GREASE: Generate factual and counterfactual explanations for GNN-based recommendations, [Online], Available: https:\/\/arxiv.org\/abs\/2208.04222, 2022."},{"key":"1519_CR160","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1145\/3477495.3531934","volume-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"S L Mu","year":"2022","unstructured":"S. L. Mu, Y. L. Li, W. X. Zhao, J. Y. Wang, B. L. Ding, J. R. Wen. Alleviating spurious correlations in knowledge-aware recommendations through counterfactual generator. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, pp. 1401\u20131411, 2022. DOI: https:\/\/doi.org\/10.1145\/3477495.3531934."},{"key":"1519_CR161","volume-title":"Improving location recommendation with urban knowledge graph","author":"C Liu","year":"2021","unstructured":"C. Liu, C. Gao, D. P. Jin, Y. Li. Improving location recommendation with urban knowledge graph, [Online], Available: https:\/\/arxiv.org\/abs\/2111.01013, 2021."},{"key":"1519_CR162","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1145\/3543507.3583321","volume-title":"Proceedings of ACM Web Conference","author":"W Z Song","year":"2023","unstructured":"W. Z. Song, S. J. Wang, Y. Wang, K. P. Liu, X. Y. Liu, M. H. Yin. A counterfactual collaborative session-based recommender system. In Proceedings of ACM Web Conference, Austin, USA, pp. 971\u2013982, 2023. DOI: https:\/\/doi.org\/10.1145\/3543507.3583321."},{"key":"1519_CR163","doi-asserted-by":"publisher","first-page":"2611","DOI":"10.1145\/3543507.3583401","volume-title":"Proceedings of ACM Web Conference","author":"H Chang","year":"2023","unstructured":"H. Chang, J. Cai, J. Li. Knowledge graph completion with counterfactual augmentation. In Proceedings of ACM Web Conference, Austin, USA, pp. 2611\u20132620, 2023. DOI: https:\/\/doi.org\/10.1145\/3543507.3583401."},{"key":"1519_CR164","doi-asserted-by":"publisher","unstructured":"W. Chen, Y. Q. Wu, Z. Zhang, F. Z. Zhuang, Z. S. He, R. B. Xie, F. Xia. FairGap: Fairness-aware recommendation via generating counterfactual graph. ACM Transactions on Information Systems, vol. 42, no. 4, Article number 94, 2024. DOI: https:\/\/doi.org\/10.1145\/3638352.","DOI":"10.1145\/3638352"},{"key":"1519_CR165","doi-asserted-by":"publisher","first-page":"3753","DOI":"10.1145\/3583780.3615165","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","author":"L Boratto","year":"2023","unstructured":"L. Boratto, F. Fabbri, G. Fenu, M. Marras, G. Medda. Counterfactual graph augmentation for consumer unfairness mitigation in recommender systems. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, UK, pp. 3753\u20133757, 2023. DOI: https:\/\/doi.org\/10.1145\/3583780.3615165."},{"key":"1519_CR166","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1145\/2623330.2623733","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"N Barbieri","year":"2014","unstructured":"N. Barbieri, F. Bonchi, G. Manco. Who to follow and why: Link prediction with explanations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 1266\u20131275, 2014. DOI: https:\/\/doi.org\/10.1145\/2623330.2623733."},{"key":"1519_CR167","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Z Q Sun","year":"2019","unstructured":"Z. Q. Sun, Z. H. Deng, J. Y. Nie, J. Tang. RotatE: Knowledge graph embedding by relational rotation in complex space. In Proceedings of the 7th International Conference on Learning Representations, New Orleans, USA, 2019."},{"key":"1519_CR168","doi-asserted-by":"publisher","unstructured":"K. Abbas, A. Abbasi, S. Dong, L. Niu, L. H. Yu, B. L. Chen, S. M. Cai, Q. Hasan. Application of network link prediction in drug discovery. BMC Bioinformatics, vol. 22, no. 1, Article number 187, 2021. DOI: https:\/\/doi.org\/10.1186\/s12859-021-04082-y.","DOI":"10.1186\/s12859-021-04082-y"},{"issue":"1","key":"1519_CR169","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/BF02289026","volume":"18","author":"L Katz","year":"1953","unstructured":"L. Katz. A new status index derived from sociometric analysis. Psychometrika, vol. 18, no. 1, pp. 39\u201343, 1953. DOI: https:\/\/doi.org\/10.1007\/BF02289026.","journal-title":"Psychometrika"},{"key":"1519_CR170","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1145\/3341161.3342897","volume-title":"Proceedings of IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining","author":"H Nassar","year":"2019","unstructured":"H. Nassar, A. R. Benson, D. F. Gleich. Pairwise link prediction. In Proceedings of IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Vancouver, Canada, pp. 386\u2013393, 2019. DOI: https:\/\/doi.org\/10.1145\/3341161.3342897."},{"key":"1519_CR171","volume-title":"Variational graph auto-","author":"T N Kipf","year":"2016","unstructured":"T. N. Kipf, M. Welling. Variational graph auto-encoders, [Online], Available: https:\/\/arxiv.org\/abs\/1611.07308,2016."},{"issue":"6","key":"1519_CR172","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","volume":"390","author":"L Y L\u00fc","year":"2011","unstructured":"L. Y. L\u00fc, T. Zhou. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, pp. 1150\u20131170, 2011. DOI: https:\/\/doi.org\/10.1016\/j.physa.2010.11.027.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"1519_CR173","doi-asserted-by":"publisher","unstructured":"V. D. Blondel, J. L. Guillaume, R. Lambiotte, E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, Article number P10008, 2008. DOI: https:\/\/doi.org\/10.1088\/1742-5468\/2008\/10\/P10008.","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"1519_CR174","first-page":"4116","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"K Hassani","year":"2020","unstructured":"K. Hassani, A. H. Khasahmadi. Contrastive multi-view representation learning on graphs. In Proceedings of the 37th International Conference on Machine Learning, pp. 4116\u20134126, 2020."},{"issue":"2","key":"1519_CR175","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S X Ji","year":"2022","unstructured":"S. X. Ji, S. R. Pan, E. Cambria, P. Marttinen, P. S. Yu. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494\u2013514, 2022. DOI: https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1519_CR176","doi-asserted-by":"publisher","first-page":"192435","DOI":"10.1109\/ACCESS.2020.3030076","volume":"8","author":"Z Chen","year":"2020","unstructured":"Z. Chen, Y. H. Wang, B. Zhao, J. Cheng, X. Zhao, Z. T. Duan. Knowledge graph completion: A review. IEEE Access, vol. 8, pp. 192435\u2013192456, 2020. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2020.3030076.","journal-title":"IEEE Access"},{"key":"1519_CR177","doi-asserted-by":"publisher","first-page":"3513","DOI":"10.1145\/3459637.3482092","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Y Wang","year":"2021","unstructured":"Y. Wang, Z. W. Liu, Z. W. Fan, L. C. Sun, P. S. Yu. DSKReG: Differentiable sampling on knowledge graph for recommendation with relational GNN. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3513\u20133517, 2021. DOI: https:\/\/doi.org\/10.1145\/3459637.3482092."},{"key":"1519_CR178","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"Proceedings of the 15th International Conference on the Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, M. Welling. Modeling relational data with graph convolutional networks. In Proceedings of the 15th International Conference on the Semantic Web, Heraklion, Greece, pp. 593\u2013607, 2018. DOI: https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38."},{"key":"1519_CR179","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"Z C Zhu","year":"2021","unstructured":"Z. C. Zhu, Z. B. Zhang, L. P. Xhonneux, J. Tang. Neural bellman-ford networks: A general graph neural network framework for link prediction. In Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021."},{"key":"1519_CR180","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1145\/3340531.3411876","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","author":"N Lim","year":"2020","unstructured":"N. Lim, B. Hooi, S. K. Ng, X. O. Wang, Y. L. Goh, R. R. Weng, J. Varadarajan. STP-UDGAT: Spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 845\u2013854, 2020. DOI: https:\/\/doi.org\/10.1145\/3340531.3411876."},{"key":"1519_CR181","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1145\/3397271.3401080","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"X C Li","year":"2020","unstructured":"X. C. Li, X. Wang, X. N. He, L. Chen, J. Xiao, T. S. Chua. Hierarchical fashion graph network for personalized outfit recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 159\u2013168, 2020. DOI: https:\/\/doi.org\/10.1145\/3397271.3401080."},{"key":"1519_CR182","volume-title":"Deoscillated graph collaborative filtering","author":"Z W Liu","year":"2020","unstructured":"Z. W. Liu, L. Meng, F. Jiang, J. W. Zhang, P. S. Yu. Deoscillated graph collaborative filtering, [Online], Available: https:\/\/arxiv.org\/abs\/2011.02100, 2020."},{"issue":"1","key":"1519_CR183","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","volume":"7","author":"G Linden","year":"2003","unstructured":"G. Linden, B. Smith, J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, vol. 7, no. 1, pp. 76\u201380, 2003. DOI: https:\/\/doi.org\/10.1109\/MIC.2003.1167344.","journal-title":"IEEE Internet Computing"},{"key":"1519_CR184","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1145\/371920.372071","volume-title":"Proceedings of the 10th International Conference on World Wide Web","author":"B M Sarwar","year":"2001","unstructured":"B. M. Sarwar, G. Karypis, J. Konstan, J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China, pp. 285\u2013295, 2001. DOI: https:\/\/doi.org\/10.1145\/371920.372071."},{"key":"1519_CR185","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1145\/2783258.2783273","volume-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"H Wang","year":"2015","unstructured":"H. Wang, N. Y. Wang, D. Y. Yeung. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, pp. 1235\u20131244, 2015. DOI: https:\/\/doi.org\/10.1145\/2783258.2783273."},{"key":"1519_CR186","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1145\/2740908.2742726","volume-title":"Proceedings of the 24th International Conference on World Wide Web","author":"S Sedhain","year":"2015","unstructured":"S. Sedhain, A. K. Menon, S. Sanner, L. X. Xie. AutoRec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp. 111\u2013112, 2015. DOI: https:\/\/doi.org\/10.1145\/2740908.2742726."},{"key":"1519_CR187","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-1-0716-2197-4_3","volume-title":"Recommender Systems Handbook","author":"Y Koren","year":"2022","unstructured":"Y. Koren, S. Rendle, R. Bell. Advances in collaborative filtering. Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, Eds., New York, USA: Springer, pp. 91\u2013142, 2022. DOI: https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_3."},{"issue":"8","key":"1519_CR188","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Y. Koren, R. Bell, C. Volinsky. Matrix factorization techniques for recommender systems. Computer, vol. 42, no. 8, pp. 30\u201337, 2009. DOI: https:\/\/doi.org\/10.1109\/MC.2009.263.","journal-title":"Computer"},{"key":"1519_CR189","doi-asserted-by":"publisher","first-page":"5474","DOI":"10.1609\/aaai.v33i01.33015474","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence","author":"T Xiao","year":"2019","unstructured":"T. Xiao, S. S. Liang, W. Z. Shen, Z. Q. Meng. Bayesian deep collaborative matrix factorization. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, USA, pp. 5474\u20135481, 2019. DOI: https:\/\/doi.org\/10.1609\/aaai.v33i01.33015474."},{"key":"1519_CR190","doi-asserted-by":"publisher","unstructured":"S. Zhang, L. Yao, A. X. Sun, Y. Tay. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, vol. 52, no. 1, Article number 5, 2020. DOI: https:\/\/doi.org\/10.1145\/3285029.","DOI":"10.1145\/3285029"},{"key":"1519_CR191","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1145\/3308558.3313442","volume-title":"Proceedings of World Wide Web Conference","author":"Q T Wu","year":"2019","unstructured":"Q. T. Wu, H. R. Zhang, X. F. Gao, P. He, P. Weng, H. Gao, G. H. Chen. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In Proceedings of World Wide Web Conference, San Francisco, USA, pp. 2091\u20132102, 2019. DOI: https:\/\/doi.org\/10.1145\/3308558.3313442."},{"key":"1519_CR192","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1145\/3292500.3330836","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"H W Wang","year":"2019","unstructured":"H. W. Wang, F. Z. Zhang, M. D. Zhang, J. Leskovec, M. Zhao, W. J. Li, Z. Y. Wang. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, USA, pp. 968\u2013977, 2019. DOI: https:\/\/doi.org\/10.1145\/3292500.3330836."},{"key":"1519_CR193","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1145\/3308558.3313488","volume-title":"Proceedings of World Wide Web Conference","author":"W Q Fan","year":"2019","unstructured":"W. Q. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. L. Tang, D. W. Yin. Graph neural networks for social recommendation. In Proceedings of World Wide Web Conference, San Francisco, USA, pp. 417\u2013426, 2019. DOI: https:\/\/doi.org\/10.1145\/3308558.3313488."},{"key":"1519_CR194","doi-asserted-by":"publisher","unstructured":"C. Gao, Y. Zheng, W. J. Wang, F. L. Feng, X. N. He, Y. Li. Causal inference in recommender systems: A survey and future directions. ACM Transactions on Information Systems, vol. 42, no. 4, Article number 88, 2024. DOI: https:\/\/doi.org\/10.1145\/3639048.","DOI":"10.1145\/3639048"},{"key":"1519_CR195","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1145\/3511808.3557431","volume-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","author":"T Xiao","year":"2022","unstructured":"T. Xiao, Z. Y. Chen, S. H. Wang. Representation matters when learning from biased feedback in recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, USA, pp. 2220\u20132229, 2022. DOI: https:\/\/doi.org\/10.1145\/3511808.3557431."},{"key":"1519_CR196","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1145\/3488560.3498521","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining","author":"T Xiao","year":"2022","unstructured":"T. Xiao, S. H. Wang. Towards unbiased and robust causal ranking for recommender systems. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 1158\u20131167, 2022. DOI: https:\/\/doi.org\/10.1145\/3488560.3498521."},{"key":"1519_CR197","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1145\/3219819.3219823","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"G R Zhou","year":"2018","unstructured":"G. R. Zhou, X. Q. Zhu, C. R. Song, Y. Fan, H. Zhu, X. Ma, Y. H. Yan, J. Q. Jin, H. Li, K. Gai. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, pp. 1059\u20131068, 2018. DOI: https:\/\/doi.org\/10.1145\/3219819.3219823."},{"key":"1519_CR198","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.24963\/ijcai.2021\/636","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"W N Zhang","year":"2021","unstructured":"W. N. Zhang, J. R. Qin, W. Guo, R. M. Tang, X. Q. He. Deep learning for click-through rate estimation. In Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 4695\u20134703, 2021. DOI: https:\/\/doi.org\/10.24963\/ijcai.2021\/636."},{"key":"1519_CR199","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1145\/3343031.3351034","volume-title":"Proceedings of the 27th ACM International Conference on Multimedia","author":"Y W Wei","year":"2019","unstructured":"Y. W. Wei, X. Wang, L. Q. Nie, X. N. He, R. C. Hong, T. S. Chua. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, pp. 1437\u20131445, 2019. DOI: https:\/\/doi.org\/10.1145\/3343031.3351034."},{"key":"1519_CR200","first-page":"2069","volume-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","author":"S S Feng","year":"2015","unstructured":"S. S. Feng, X. T. Li, Y. F. Zeng, G. Cong, Y. M. Chee, Q. Yuan. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, pp. 2069\u20132075, 2015."},{"issue":"11","key":"1519_CR201","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TKDE.2017.2741484","volume":"29","author":"H Z Yin","year":"2017","unstructured":"H. Z. Yin, W. Q. Wang, H. Wang, L. Chen, X. F. Zhou. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 11, pp. 2537\u20132551, 2017. DOI: https:\/\/doi.org\/10.1109\/TKDE.2017.2741484.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR202","volume-title":"A survey of point-of-interest recommendation in location-based social networks","author":"S L Zhao","year":"2016","unstructured":"S. L. Zhao, I. King, M. R. Lyu. A survey of point-of-interest recommendation in location-based social networks, [Online], Available: https:\/\/arxiv.org\/abs\/1607.00647,2016."},{"key":"1519_CR203","volume-title":"Reinforcement Learning: An Introduction","author":"R S Sutton","year":"1998","unstructured":"R. S. Sutton, A. G. Barto. Reinforcement Learning: An Introduction, Cambridge, USA: MIT Press, Article number 1054, 1998."},{"key":"1519_CR204","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"F Baradel","year":"2020","unstructured":"F. Baradel, N. Neverova, J. Mille, G. Mori, C. Wolf. Co-Phy: Counterfactual learning of physical dynamics. In Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020."},{"key":"1519_CR205","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Y Z Li","year":"2020","unstructured":"Y. Z. Li, A. Torralba, A. Anandkumar, D. Fox, A. Garg. Causal discovery in physical systems from videos. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020."},{"key":"1519_CR206","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.1609\/AAAI.V36I2.20044","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","author":"Z Z Li","year":"2022","unstructured":"Z. Z. Li, X. Y. Zhu, Z. Lei, Z. X. Zhang. Deconfounding physical dynamics with global causal relation and confounder transmission for counterfactual prediction. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, pp. 1536\u20131545, 2022. DOI: https:\/\/doi.org\/10.1609\/AAAI.V36I2.20044."},{"key":"1519_CR207","volume-title":"Towards explainable motion prediction using heterogeneous graph representations","author":"S C Limeros","year":"2022","unstructured":"S. C. Limeros, S. Majchrowska, J. Johnander, C. Petersson, D. F. Llorca. Towards explainable motion prediction using heterogeneous graph representations, [Online], Available: https:\/\/arxiv.org\/abs\/2212.03806, 2022."},{"key":"1519_CR208","volume-title":"Estimating counterfactual treatment outcomes over time in complex multiagent scenarios","author":"K Fujii","year":"2022","unstructured":"K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, K. Takeda. Estimating counterfactual treatment outcomes over time in complex multiagent scenarios, [Online], Available: https:\/\/arxiv.org\/abs\/2206.01900, 2022."},{"key":"1519_CR209","volume-title":"Counterfactual multi-agent reinforcement learning with graph convolution communication","author":"J Y Su","year":"2020","unstructured":"J. Y. Su, S. Adams, P. A. Beling. Counterfactual multi-agent reinforcement learning with graph convolution communication, [Online], Available: https:\/\/arxiv.org\/abs\/2004.00470, 2020."},{"key":"1519_CR210","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC48557.2020.9154336","volume-title":"Proceedings of the 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications","author":"N Naderializadeh","year":"2020","unstructured":"N. Naderializadeh, M. Eisen, A. Ribeiro. Wireless power control via counterfactual optimization of graph neural networks. In Proceedings of the 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, Atlanta, USA, 2020. DOI: https:\/\/doi.org\/10.1109\/SPAWC48557.2020.9154336."},{"key":"1519_CR211","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.18653\/v1\/2021.naacl-main.156","volume-title":"Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"H R Wu","year":"2021","unstructured":"H. R. Wu, W. Chen, S. Xu, B. Xu. Counterfactual supporting facts extraction for explainable medical record based diagnosis with graph network. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1942\u20131955, 2021. DOI: https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.156."},{"key":"1519_CR212","first-page":"259","volume-title":"Proceedings of the 2nd Machine Learning for Health Symposium","author":"R Xu","year":"2022","unstructured":"R. Xu, Y. Yu, C. Zhang, M. K. Ali, J. C. Ho, C. Yang. Counterfactual and factual reasoning over hypergraphs for interpretable clinical predictions on EHR. In Proceedings of the 2nd Machine Learning for Health Symposium, New Orleans, USA, pp. 259\u2013278, 2022."},{"key":"1519_CR213","doi-asserted-by":"publisher","unstructured":"B. L. Zhang, X. X. Guo, Q. F. Lin, H. R. Wang, S. B. Xu. Counterfactual inference graph network for disease prediction. Knowledge-Based Systems, vol. 255, Article number 109722, 2022. DOI: https:\/\/doi.org\/10.1016\/j.knosys.2022.109722.","DOI":"10.1016\/j.knosys.2022.109722"},{"issue":"1","key":"1519_CR214","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1039\/D2DD00045H","volume":"2","author":"S Y Qin","year":"2023","unstructured":"S. Y. Qin, S. L Jiang, J. P. Li, P. Balaprakash, R. C. Van Lehn, V. M. Zavala. Capturing molecular interactions in graph neural networks: A case study in multi-component phase equilibrium. Digital Discovery, vol. 2, no. 1, pp. 138\u2013151, 2023. DOI: https:\/\/doi.org\/10.1039\/D2DD00045H.","journal-title":"Digital Discovery"},{"issue":"13","key":"1519_CR215","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.1039\/D1SC05259D","volume":"13","author":"G P Wellawatte","year":"2022","unstructured":"G. P. Wellawatte, A. Seshadri, A. D. White. Model agnostic generation of counterfactual explanations for molecules. Chemical Science, vol. 13, no. 13, pp. 3697\u20133705, 2022. DOI: https:\/\/doi.org\/10.1039\/D1SC05259D.","journal-title":"Chemical Science"},{"key":"1519_CR216","doi-asserted-by":"publisher","unstructured":"J. R. Li, Y. Horiguchi, T. Sawaragi. Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge-relational feature transfer learning algorithm. Advanced Engineering Informatics, vol. 51, Article number 101516, 2022. DOI: https:\/\/doi.org\/10.1016\/J.AEI.2021.101516.","DOI":"10.1016\/J.AEI.2021.101516"},{"key":"1519_CR217","doi-asserted-by":"publisher","unstructured":"D. Pham, Y. F. Zhang. Counterfactual based reinforcement learning for graph neural networks. Annals of Operations Research, published online. DOI: https:\/\/doi.org\/10.1007\/s10479-022-04978-9.","DOI":"10.1007\/s10479-022-04978-9"},{"issue":"10","key":"1519_CR218","doi-asserted-by":"publisher","first-page":"10540","DOI":"10.1109\/TKDE.2023.3250523","volume":"35","author":"C J Xiao","year":"2023","unstructured":"C. J. Xiao, X. Xu, Y. Lei, K. P. Zhang, S. Y. Liu, F. Zhou. Counterfactual graph learning for anomaly detection on attributed networks. IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 10540\u201310553, 2023. DOI: https:\/\/doi.org\/10.1109\/TKDE.2023.3250523.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR219","doi-asserted-by":"publisher","first-page":"23682","DOI":"10.1609\/aaai.v38i21.30524","volume-title":"Proceedings of the 38th AAAI Conference on Artificial Intelligence","author":"Y T Wei","year":"2024","unstructured":"Y. T. Wei, W. Z. Shu, Z. T. Cheng, W. X. Tai, C. J. Xiao, T. Zhong. Counterfactual graph learning for anomaly detection with feature disentanglement and generation (student abstract). In Proceedings of the 38th AAAI Conference on Artificial Intelligence, Vancouver, Canada, pp. 23682\u201323683, 2024. DOI: https:\/\/doi.org\/10.1609\/aaai.v38i21.30524."},{"key":"1519_CR220","doi-asserted-by":"publisher","first-page":"137957","DOI":"10.1109\/ACCESS.2021.3118224","volume":"9","author":"M Gulzar","year":"2021","unstructured":"M. Gulzar, Y. Muhammad, N. Muhammad. A survey on motion prediction of pedestrians and vehicles for autonomous driving. IEEE Access, vol. 9, pp. 137957\u2013137969, 2021. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2021.3118224.","journal-title":"IEEE Access"},{"key":"1519_CR221","doi-asserted-by":"publisher","first-page":"28573","DOI":"10.1109\/ACCESS.2018.2831228","volume":"6","author":"A Dorri","year":"2018","unstructured":"A. Dorri, S. S. Kanhere, R. Jurdak. Multi-agent systems: A survey. IEEE Access, vol. 6, pp. 28573\u201328593, 2018. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2018.2831228.","journal-title":"IEEE Access"},{"key":"1519_CR222","doi-asserted-by":"publisher","first-page":"2974","DOI":"10.1609\/AAAI.V32I1.11794","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"J Foerster","year":"2018","unstructured":"J. Foerster, G. Farquhar, T. Afouras, N. Nardelli, S. Whiteson. Counterfactual multi-agent policy gradients. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 2974\u20132982, 2018. DOI: https:\/\/doi.org\/10.1609\/AAAI.V32I1.11794."},{"key":"1519_CR223","doi-asserted-by":"publisher","unstructured":"S. C. Huang, A. Pareek, S. Seyyedi, I. Banerjee, M. P. Lungren. Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines. npj Digital Medicine, vol. 3, Article number 136, 2020. DOI: https:\/\/doi.org\/10.1038\/S41746-020-00341-z.","DOI":"10.1038\/S41746-020-00341-z"},{"key":"1519_CR224","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.inffus.2021.01.008","volume":"71","author":"A Holzinger","year":"2021","unstructured":"A. Holzinger, B. Malle, A. Saranti, B. Pfeifer. Towards multi-modal causability with graph neural networks enabling information fusion for explainable AI. Information Fusion, vol. 71, pp. 28\u201337, 2021. DOI: https:\/\/doi.org\/10.1016\/J.INFFUS.2021.01.008.","journal-title":"Information Fusion"},{"key":"1519_CR225","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","volume":"37","author":"O Wieder","year":"2020","unstructured":"O. Wieder, S. Kohlbacher, M. Kuenemann, A. Garon, P. Ducrot, T. Seidel, T. Langer. A compact review of molecular property prediction with graph neural networks. Drug Discovery Today: Technologies, vol. 37, pp. 1\u201312, 2020. DOI: https:\/\/doi.org\/10.1016\/j.ddtec.2020.11.009.","journal-title":"Drug Discovery Today: Technologies"},{"issue":"3","key":"1519_CR226","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1021\/acs.jcim.2c01091","volume":"63","author":"A R N Aouichaoui","year":"2023","unstructured":"A. R. N. Aouichaoui, F. Fan, S. S. Mansouri, J. Abildskov, G. Sin. Combining group-contribution concept and graph neural networks toward interpretable molecular property models. Journal of Chemical Information and Modeling, vol. 63, no. 3, pp. 725\u2013744, 2023. DOI: https:\/\/doi.org\/10.1021\/acs.jcim.2c01091.","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"12","key":"1519_CR227","doi-asserted-by":"publisher","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","volume":"35","author":"X X Ma","year":"2023","unstructured":"X. X. Ma, J. Wu, S. Xue, J. Yang, C. Zhou, Q. Z. Sheng, H. Xiong, L. Akoglu. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12012\u201312038, 2023. DOI: https:\/\/doi.org\/10.1109\/TKDE.2021.3118815.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1519_CR228","doi-asserted-by":"publisher","unstructured":"C. Agarwal, O. Queen, H. Lakkaraju, M. Zitnik. Evaluating explainability for graph neural networks. Scientific Data, vol. 10, no. 1, Article number 144, 2023. DOI: https:\/\/doi.org\/10.1038\/s41597-023-01974-x.","DOI":"10.1038\/s41597-023-01974-x"},{"key":"1519_CR229","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"J Deng","year":"2009","unstructured":"J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, F.-F. Li. ImageNet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 248\u2013255, 2009. DOI: https:\/\/doi.org\/10.1109\/CVPR.2009.5206848."},{"issue":"11","key":"1519_CR230","doi-asserted-by":"publisher","first-page":"40","DOI":"10.22215\/timreview\/1282","volume":"9","author":"M Westerlund","year":"2019","unstructured":"M. Westerlund. The emergence of deepfake technology: A review. Technology Innovation Management Review, vol. 9, no. 11, pp. 40\u201353, 2019. DOI: https:\/\/doi.org\/10.22215\/timreview\/1282.","journal-title":"Technology Innovation Management Review"},{"key":"1519_CR231","doi-asserted-by":"publisher","first-page":"3377","DOI":"10.1145\/3308558.3313603","volume-title":"Proceedings of World Wide Web Conference","author":"T Xiao","year":"2019","unstructured":"T. Xiao, S. S. Liang, Z. Q. Meng. Hierarchical neural variational model for personalized sequential recommendation. In Proceedings of World Wide Web Conference, San Francisco, USA, pp. 3377\u20133383, 2019. DOI: https:\/\/doi.org\/10.1145\/3308558.3313603."},{"key":"1519_CR232","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"T Xiao","year":"2022","unstructured":"T. Xiao, Z. Y. Chen, Z. M. Guo, Z. Y. Zhuang, S. H. Wang. Decoupled self-supervised learning for graphs. In Proceedings of the 36th International Conference on Neural Information Processing Systems, New Orleans, USA, 2022."},{"key":"1519_CR233","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1109\/SOCIALCOM.2010.33","volume-title":"Proceedings of IEEE Second International Conference on Social Computing","author":"B Suh","year":"2010","unstructured":"B. Suh, L. C. Hong, P. Pirolli, E. H. Chi. Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In Proceedings of IEEE Second International Conference on Social Computing, Minneapolis, USA, pp. 177\u2013184, 2010. DOI: https:\/\/doi.org\/10.1109\/SOCIALCOM.2010.33."},{"key":"1519_CR234","volume-title":"Wiki-CS: A Wikipedia-based benchmark for graph neural networks","author":"P Mernyei","year":"2020","unstructured":"P. Mernyei, C. Cangea. Wiki-CS: A Wikipedia-based benchmark for graph neural networks, [Online], Available: https:\/\/arxiv.org\/abs\/2007.02901, 2020."},{"issue":"6","key":"1519_CR235","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s10791-018-9340-3","volume":"22","author":"W Shalaby","year":"2019","unstructured":"W. Shalaby, W. Zadrozny, H. X. Jin. Beyond word embeddings: Learning entity and concept representations from large scale knowledge bases. Information Retrieval Journal, vol. 22, no. 6, pp. 525\u2013542, 2019. DOI: https:\/\/doi.org\/10.1007\/S10791-018-9340-3.","journal-title":"Information Retrieval Journal"},{"key":"1519_CR236","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1145\/3269206.3269252","volume-title":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","author":"R J Wang","year":"2018","unstructured":"R. J. Wang, Y. C. Yan, J. L. Wang, Y. T. Jia, Y. Zhang, W. N. Zhang, X. B. Wang. AceKG: A large-scale knowledge graph for academic data mining. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, pp. 1487\u20131490, 2018. DOI: https:\/\/doi.org\/10.1145\/3269206.3269252."},{"key":"1519_CR237","volume-title":"SIGN: Scalable inception graph neural networks","author":"F Frasca","year":"2020","unstructured":"F. Frasca, E. Rossi, D. Eynard, B. Chamberlain, M. Bronstein, F. Monti. SIGN: Scalable inception graph neural networks, [Online], Available: https:\/\/arxiv.org\/abs\/2004.11198, 2020."},{"issue":"70","key":"1519_CR238","first-page":"1","volume":"21","author":"S M Kazemi","year":"2020","unstructured":"S. M. Kazemi, R. Goel, K. Jain, I. Kobyzev, A. Sethi, P. Forsyth, P. Poupart. Representation learning for dynamic graphs: A survey. Journal of Machine Learning Research, vol. 21, no. 70, pp. 1\u201373, 2020.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"1519_CR239","doi-asserted-by":"publisher","first-page":"89","DOI":"10.2307\/30040691","volume":"28","author":"J E Perry-Smith","year":"2003","unstructured":"J. E. Perry-Smith, C. E. Shalley. The social side of creativity: A static and dynamic social network perspective. The Academy of Management Review, vol. 28, no. 1, pp. 89\u2013106, 2003. DOI: https:\/\/doi.org\/10.2307\/30040691.","journal-title":"The Academy of Management Review"},{"key":"1519_CR240","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-80230-0","volume-title":"Modeling Dynamic Transportation Networks: An Intelligent Transportation System Oriented Approach","author":"B Ran","year":"1996","unstructured":"B. Ran, D. Boyce. Modeling Dynamic Transportation Networks: An Intelligent Transportation System Oriented Approach, Berlin, Germany: Springer, 1996. DOI: https:\/\/doi.org\/10.1007\/978-3-642-80230-0."},{"key":"1519_CR241","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1145\/3357384.3357901","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"T Xiao","year":"2019","unstructured":"T. Xiao, S. S. Liang, Z. Q. Meng. Dynamic collaborative recurrent learning. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, pp. 1151\u20131160, 2019. DOI: https:\/\/doi.org\/10.1145\/3357384.3357901."},{"key":"1519_CR242","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.1145\/3357384.3358057","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"T Xiao","year":"2019","unstructured":"T. Xiao, J. X. Ren, Z. Q. Meng, H. Sun, S. S. Liang. Dynamic Bayesian metric learning for personalized product search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China, pp. 1693\u20131702, 2019. DOI: https:\/\/doi.org\/10.1145\/3357384.3358057."},{"key":"1519_CR243","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Y N You","year":"2020","unstructured":"Y. N. You, T. L. Chen, Y. D. Sui, T. Chen, Z. Y. Wang, Y. Shen. Graph contrastive learning with augmentations. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020."},{"key":"1519_CR244","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"H R Zhang","year":"2021","unstructured":"H. R. Zhang, Q. T. Wu, J. C. Yan, D. Wipf, P. S. Yu. From canonical correlation analysis to self-supervised graph neural networks. In Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021."},{"key":"1519_CR245","volume-title":"Desiderata for representation learning: A causal perspective","author":"Y X Wang","year":"2021","unstructured":"Y. X. Wang, M. I. Jordan. Desiderata for representation learning: A causal perspective, [Online], Available: https:\/\/arxiv.org\/abs\/2109.03795, 2021."}],"container-title":["Machine Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-024-1519-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11633-024-1519-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-024-1519-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T05:11:55Z","timestamp":1737695515000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11633-024-1519-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":245,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1519"],"URL":"https:\/\/doi.org\/10.1007\/s11633-024-1519-z","relation":{},"ISSN":["2731-538X","2731-5398"],"issn-type":[{"value":"2731-538X","type":"print"},{"value":"2731-5398","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]},"assertion":[{"value":"10 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declared that they have no conflicts of interest to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations of conflict of interest"}}]}}