{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T22:14:27Z","timestamp":1784585667236,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF21-1-0198"],"award-info":[{"award-number":["W911NF21-1-0198"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DHS CINA","award":["E205949D"],"award-info":[{"award-number":["E205949D"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1707548 and IIS-1909702"],"award-info":[{"award-number":["IIS-1707548 and IIS-1909702"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,27]]},"DOI":"10.1145\/3539597.3570421","type":"proceedings-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T23:27:00Z","timestamp":1677108420000},"page":"634-642","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Towards Faithful and Consistent Explanations for Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4504-7809","authenticated-orcid":false,"given":"Tianxiang","family":"Zhao","sequence":"first","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4192-0826","authenticated-orcid":false,"given":"Dongsheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0940-6595","authenticated-orcid":false,"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3448-4878","authenticated-orcid":false,"given":"Suhang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, State College, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,27]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in neural information processing systems. 1993--2001."},{"key":"e_1_3_2_1_2_1","volume-title":"Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686","author":"Baldassarre Federico","year":"2019","unstructured":"Federico Baldassarre and Hossein Azizpour. 2019. Explainability techniques for graph convolutional networks. arXiv preprint arXiv:1905.13686 (2019)."},{"key":"e_1_3_2_1_3_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)."},{"key":"e_1_3_2_1_4_1","unstructured":"Hryhorii Chereda A. Bleckmann F. Kramer A. Leha and T. Bei\u00dfbarth. 2019. Utilizing Molecular Network Information via Graph Convolutional Neural Networks to Predict Metastatic Event in Breast Cancer. Studies in health technology and informatics Vol. 267 (2019) 181--186."},{"key":"e_1_3_2_1_5_1","volume-title":"Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. arXiv preprint arXiv:2201.00232","author":"Dai Enyan","year":"2022","unstructured":"Enyan Dai, Wei Jin, Hui Liu, and Suhang Wang. 2022a. Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels. arXiv preprint arXiv:2201.00232 (2022)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482306"},{"key":"e_1_3_2_1_7_1","volume-title":"Robustness, Fairness, and Explainability. arXiv preprint arXiv:2204.08570","author":"Dai Enyan","year":"2022","unstructured":"Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2022b. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. arXiv preprint arXiv:2204.08570 (2022)."},{"key":"e_1_3_2_1_8_1","unstructured":"David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems. 2224--2232."},{"key":"e_1_3_2_1_9_1","unstructured":"Martin Ester Hans-Peter Kriegel J\u00f6rg Sander Xiaowei Xu et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.. In kdd Vol. 96. 226--231."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467283"},{"key":"e_1_3_2_1_11_1","volume-title":"Graph Neural Networks for Social Recommendation. The World Wide Web Conference","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan, Y. Ma, Qing Li, Yuan He, Y. Zhao, Jiliang Tang, and D. Yin. 2019. Graph Neural Networks for Social Recommendation. The World Wide Web Conference (2019)."},{"key":"e_1_3_2_1_12_1","unstructured":"Justin Gilmer Samuel S Schoenholz Patrick F Riley Oriol Vinyals and George E Dahl. 2017. Neural Message Passing for Quantum Chemistry. In ICML."},{"key":"e_1_3_2_1_13_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Graphlime: Local interpretable model explanations for graph neural networks. arXiv preprint arXiv:2001.06216","author":"Huang Qiang","year":"2020","unstructured":"Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang. 2020. Graphlime: Local interpretable model explanations for graph neural networks. arXiv preprint arXiv:2001.06216 (2020)."},{"key":"e_1_3_2_1_15_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3191086"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning. PMLR, 6666--6679","author":"Lin Wanyu","year":"2021","unstructured":"Wanyu Lin, Hao Lan, and Baochun Li. 2021. Generative causal explanations for graph neural networks. In International Conference on Machine Learning. PMLR, 6666--6679."},{"key":"e_1_3_2_1_18_1","volume-title":"Parameterized explainer for graph neural network. Advances in neural information processing systems","author":"Luo Dongsheng","year":"2020","unstructured":"Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. Advances in neural information processing systems, Vol. 33 (2020), 19620--19631."},{"key":"e_1_3_2_1_19_1","volume-title":"Scientific Reports","volume":"9","author":"Mansimov Elman","year":"2019","unstructured":"Elman Mansimov, O. Mahmood, Seokho Kang, and Kyunghyun Cho. 2019. Molecular Geometry Prediction using a Deep Generative Graph Neural Network. Scientific Reports, Vol. 9 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. arXiv preprint arXiv:2201.08164","author":"Nauta Meike","year":"2022","unstructured":"Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, J\u00f6rg Schl\u00f6tterer, Maurice van Keulen, and Christin Seifert. 2022. From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. arXiv preprint arXiv:2201.08164 (2022)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"e_1_3_2_1_22_1","volume-title":"Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction. arXiv preprint arXiv:2107.04119","author":"Rao Jiahua","year":"2021","unstructured":"Jiahua Rao, Shuangjia Zheng, and Yuedong Yang. 2021. Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction. arXiv preprint arXiv:2107.04119 (2021)."},{"key":"e_1_3_2_1_23_1","volume-title":"Higher-order explanations of graph neural networks via relevant walks. arXiv preprint arXiv:2006.03589","author":"Schnake Thomas","year":"2020","unstructured":"Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Sch\u00fctt, Klaus-Robert M\u00fcller, and Gr\u00e9goire Montavon. 2020. Higher-order explanations of graph neural networks via relevant walks. arXiv preprint arXiv:2006.03589 (2020)."},{"key":"e_1_3_2_1_24_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Shan Caihua","year":"2021","unstructured":"Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li. 2021. Reinforcement Learning Enhanced Explainer for Graph Neural Networks. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_25_1","volume-title":"Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. ArXiv","author":"Sorokin Daniil","year":"2018","unstructured":"Daniil Sorokin and Iryna Gurevych. 2018. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering. ArXiv, Vol. abs\/1808.04126 (2018)."},{"key":"e_1_3_2_1_26_1","volume-title":"ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations. ArXiv","author":"Tang S.","year":"2019","unstructured":"S. Tang, Bo Li, and Haijun Yu. 2019. ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations. ArXiv, Vol. abs\/1911.05467 (2019)."},{"key":"e_1_3_2_1_27_1","volume-title":"The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning","author":"Tsamardinos Ioannis","year":"2006","unstructured":"Ioannis Tsamardinos, Laura E Brown, and Constantin F Aliferis. 2006. The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning, Vol. 65, 1 (2006), 31--78."},{"key":"e_1_3_2_1_28_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_29_1","volume-title":"Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. arXiv preprint arXiv:2010.05788","author":"Vu Minh N","year":"2020","unstructured":"Minh N Vu and My T Thai. 2020. Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. arXiv preprint arXiv:2010.05788 (2020)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_1_31_1","unstructured":"Xiang Wang Yingxin Wu An Zhang Xiangnan He and Tat-seng Chua. 2020b. Causal Screening to Interpret Graph Neural Networks. (2020)."},{"key":"e_1_3_2_1_32_1","volume-title":"Discovering Invariant Rationales for Graph Neural Networks. arXiv preprint arXiv:2201.12872","author":"Wu Ying-Xin","year":"2022","unstructured":"Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022. Discovering Invariant Rationales for Graph Neural Networks. arXiv preprint arXiv:2201.12872 (2022)."},{"key":"e_1_3_2_1_33_1","volume-title":"Decoupled Self-supervised Learning for Non-Homophilous Graphs. arXiv e-prints","author":"Xiao Teng","year":"2022","unstructured":"Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. 2022. Decoupled Self-supervised Learning for Non-Homophilous Graphs. arXiv e-prints (2022), arXiv--2206."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467451"},{"key":"e_1_3_2_1_35_1","volume-title":"HP-GMN: Graph Memory Networks for Heterophilous Graphs. arXiv preprint arXiv:2210.08195","author":"Xu Junjie","year":"2022","unstructured":"Junjie Xu, Enyan Dai, Xiang Zhang, and Suhang Wang. 2022. HP-GMN: Graph Memory Networks for Heterophilous Graphs. arXiv preprint arXiv:2210.08195 (2022)."},{"key":"e_1_3_2_1_36_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_37_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems","author":"Ying Rex","year":"2019","unstructured":"Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, Vol. 32 (2019), 9240."},{"key":"e_1_3_2_1_38_1","volume-title":"Explainability in graph neural networks: A taxonomic survey. arXiv preprint arXiv:2012.15445","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020. Explainability in graph neural networks: A taxonomic survey. arXiv preprint arXiv:2012.15445 (2020)."},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Machine Learning. PMLR, 12241--12252","author":"Yuan Hao","year":"2021","unstructured":"Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021. On explainability of graph neural networks via subgraph explorations. In International Conference on Machine Learning. PMLR, 12241--12252."},{"key":"e_1_3_2_1_40_1","volume-title":"Link prediction based on graph neural networks. Advances in neural information processing systems","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_41_1","volume-title":"ProtGNN: Towards Self-Explaining Graph Neural Networks. arXiv preprint arXiv:2112.00911","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee. 2021. ProtGNN: Towards Self-Explaining Graph Neural Networks. arXiv preprint arXiv:2112.00911 (2021)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411977"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511929"}],"event":{"name":"WSDM '23: The Sixteenth ACM International Conference on Web Search and Data Mining","location":"Singapore Singapore","acronym":"WSDM '23","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539597.3570421","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539597.3570421","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539597.3570421","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:14Z","timestamp":1750186934000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539597.3570421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,27]]},"references-count":44,"alternative-id":["10.1145\/3539597.3570421","10.1145\/3539597"],"URL":"https:\/\/doi.org\/10.1145\/3539597.3570421","relation":{},"subject":[],"published":{"date-parts":[[2023,2,27]]},"assertion":[{"value":"2023-02-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}