{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:45:43Z","timestamp":1771267543278,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","funder":[{"name":"NSF (National Science Foundation)","award":["2046086"],"award-info":[{"award-number":["2046086"]}]},{"name":"NSF (National Science Foundation)","award":["2431569"],"award-info":[{"award-number":["2431569"]}]},{"name":"NSF (National Science Foundation)","award":["2112650"],"award-info":[{"award-number":["2112650"]}]},{"name":"National Institute of Food and Agriculture","award":["2020-69012-31914"],"award-info":[{"award-number":["2020-69012-31914"]}]},{"name":"São Paulo Research Foundation","award":["20\/09835-1"],"award-info":[{"award-number":["20\/09835-1"]}]},{"name":"São Paulo Research Foundation","award":["22\/02176-8"],"award-info":[{"award-number":["22\/02176-8"]}]},{"name":"São Paulo Research Foundation","award":["22\/09091-8"],"award-info":[{"award-number":["22\/09091-8"]}]},{"name":"National Council for Scientific and Technological Development","award":["303588\/2022-5"],"award-info":[{"award-number":["303588\/2022-5"]}]},{"name":"National Council for Scientific and Technological Development","award":["406417\/2022-9"],"award-info":[{"award-number":["406417\/2022-9"]}]},{"name":"Coordination for the Improvement of Higher Education Personnel","award":["001"],"award-info":[{"award-number":["001"]}]},{"name":"Coordination for the Improvement of Higher Education Personnel","award":["41\/2017"],"award-info":[{"award-number":["41\/2017"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,2,22]]},"DOI":"10.1145\/3773966.3777968","type":"proceedings-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:50:01Z","timestamp":1771264201000},"page":"563-573","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-view Graph Condensation via Tensor Decomposition"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7225-7974","authenticated-orcid":false,"given":"Nicolas","family":"Roque dos Santos","sequence":"first","affiliation":[{"name":"University of California, Riverside, Riverside, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9268-4058","authenticated-orcid":false,"given":"Dawon","family":"Ahn","sequence":"additional","affiliation":[{"name":"University of California, Riverside, Riverside, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4735-0337","authenticated-orcid":false,"given":"Diego","family":"Minatel","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3112-4746","authenticated-orcid":false,"given":"Alneu","family":"de Andrade Lopes","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, S\u00e3o Carlos, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-8483","authenticated-orcid":false,"given":"Evangelos","family":"Papalexakis","sequence":"additional","affiliation":[{"name":"University of California, Riverside, Riverside, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.12.124"},{"key":"e_1_3_2_1_2_1","first-page":"8","article-title":"Spectral sparsification of graphs: theory and algorithms","volume":"56","author":"Batson Joshua","year":"2013","unstructured":"Joshua Batson, Daniel A. Spielman, Nikhil Srivastava, and Shang-Hua Teng. 2013. Spectral sparsification of graphs: theory and algorithms. Commun. ACM, Vol. 56, 8 (aug 2013), 87\u201394.","journal-title":"Commun. ACM"},{"key":"e_1_3_2_1_3_1","unstructured":"Lukas Biewald. 2020. Experiment Tracking with Weights and Biases. https:\/\/www.wandb.com\/ Software available from wandb.com."},{"key":"e_1_3_2_1_4_1","volume-title":"International conference on machine learning. PMLR, 1597-1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597-1607."},{"key":"e_1_3_2_1_5_1","volume-title":"Gradzip: Gradient compression using alternating matrix factorization for large-scale deep learning. In NeurIPS.","author":"Cho Minsik","year":"2019","unstructured":"Minsik Cho, Vinod Muthusamy, Brad Nemanich, and Ruchir Puri. 2019. Gradzip: Gradient compression using alternating matrix factorization for large-scale deep learning. In NeurIPS."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/00018732.2011.572452"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"e_1_3_2_1_8_1","first-page":"3844","article-title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurIPS. Curran Associates","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NeurIPS. Curran Associates, Inc., 3844-3852.","journal-title":"Inc."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Austin Derrow-Pinion Jennifer She David Wong Oliver Lange Todd Hester Luis Perez Marc Nunkesser Seongjae Lee Xueying Guo Brett Wiltshire Peter W. Battaglia Vishal Gupta Ang Li Zhongwen Xu Alvaro Sanchez-Gonzalez Yujia Li and Petar Velickovic. 2021. ETA Prediction with Graph Neural Networks in Google Maps. In CIKM. ACM.","DOI":"10.1145\/3459637.3481916"},{"key":"e_1_3_2_1_10_1","volume-title":"Companion Proceedings of the ACM Web Conference 2024","author":"Dickens Charles","year":"2024","unstructured":"Charles Dickens, Edward Huang, Aishwarya Reganti, Jiong Zhu, Karthik Subbian, and Danai Koutra. 2024. Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training. In Companion Proceedings of the ACM Web Conference 2024 (Singapore, Singapore) (WWW '24). Association for Computing Machinery, New York, NY, USA, 1502\u20131510."},{"key":"e_1_3_2_1_11_1","volume-title":"KDD (Philadelphia, PA, USA)","author":"Ding Chris","unstructured":"Chris Ding, Tao Li, Wei Peng, and Haesun Park. 2006. Orthogonal nonnegative matrix t-factorizations for clustering. In KDD (Philadelphia, PA, USA). Association for Computing Machinery, New York, NY, USA, 126\u2013135."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575646"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA61799.2024.10722822"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the ACM on Web Conference 2025 (Sydney NSW, Australia) (WWW '25)","author":"Gao Xinyi","year":"2025","unstructured":"Xinyi Gao, Guanhua Ye, Tong Chen, Wentao Zhang, Junliang Yu, and Hongzhi Yin. 2025. Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition. In Proceedings of the ACM on Web Conference 2025 (Sydney NSW, Australia) (WWW '25). Association for Computing Machinery, New York, NY, USA, 4359\u20134373."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_17_1","volume-title":"Papalexakis","author":"Gujral Ekta","year":"2018","unstructured":"Ekta Gujral and Evangelos E. Papalexakis. 2018. SMACD: Semi-supervised Multi-Aspect Community Detection. In Proceedings of the 2018 SIAM International Conference on Data Mining (SDM). 702-710."},{"key":"e_1_3_2_1_18_1","volume-title":"NeurIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS, Vol. 30 (2017)."},{"key":"e_1_3_2_1_19_1","volume-title":"MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. In The Eleventh International Conference on Learning Representations.","author":"Han Xiaotian","year":"2023","unstructured":"Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah. 2023. MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_20_1","volume-title":"International Joint Conference on Artificial Intelligence (IJCAI)","author":"Hashemi Mohammad","year":"2024","unstructured":"Mohammad Hashemi, Shengbo Gong, Juntong Ni, Wenqi Fan, B Aditya Prakash, and Wei Jin. 2024. A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation. International Joint Conference on Artificial Intelligence (IJCAI) (2024)."},{"key":"e_1_3_2_1_21_1","unstructured":"Mingguo He Zhewei Wei Zengfeng Huang and Hongteng Xu. 2021. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. In Advances in Neural Information Processing Systems A. Beygelzimer Y. Dauphin P. Liang and J. Wortman Vaughan (Eds.)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339723"},{"key":"e_1_3_2_1_23_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22118-22133."},{"key":"e_1_3_2_1_24_1","first-page":"6021","article-title":"High-order pooling for graph neural networks with tensor decomposition","volume":"35","author":"Hua Chenqing","year":"2022","unstructured":"Chenqing Hua, Guillaume Rabusseau, and Jian Tang. 2022. High-order pooling for graph neural networks with tensor decomposition. Advances in Neural Information Processing Systems, Vol. 35 (2022), 6021-6033.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2285514"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"e_1_3_2_1_28_1","volume-title":"Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations.","author":"Jin Wei","year":"2022","unstructured":"Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. 2022b. Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/195058.195422"},{"key":"e_1_3_2_1_30_1","volume-title":"Adam: A Method for Stochastic Optimization. In ICLR, 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR, 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings."},{"key":"e_1_3_2_1_31_1","series-title":"SIAM review","volume-title":"Tensor decompositions and applications","author":"Kolda Tamara G","year":"2009","unstructured":"Tamara G Kolda and Brett W Bader. 2009. Tensor decompositions and applications. SIAM review, Vol. 51, 3 (2009), 455-500."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Da Kuang Chris Ding and Haesun Park. [n.d.]. Symmetric Nonnegative Matrix Factorization for Graph Clustering. 106-117.","DOI":"10.1137\/1.9781611972825.10"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.04.003"},{"key":"e_1_3_2_1_34_1","volume-title":"Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697","author":"Liu Mengyang","year":"2022","unstructured":"Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song. 2022. Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697 (2022)."},{"key":"e_1_3_2_1_35_1","volume-title":"Graph Distillation with Eigenbasis Matching. In Forty-first International Conference on Machine Learning.","author":"Liu Yang","year":"2024","unstructured":"Yang Liu, Deyu Bo, and Chuan Shi. 2024a. Graph Distillation with Eigenbasis Matching. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Liu Zhanyu","year":"2024","unstructured":"Zhanyu Liu, Chaolv Zeng, and Guanjie Zheng. 2024b. Graph Data Condensation via Self-expressive Graph Structure Reconstruction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Barcelona, Spain) (KDD '24). Association for Computing Machinery, New York, NY, USA, 1992\u20132002."},{"key":"e_1_3_2_1_37_1","first-page":"1","article-title":"Graph reduction with spectral and cut guarantees","volume":"20","author":"Loukas Andreas","year":"2019","unstructured":"Andreas Loukas. 2019. Graph reduction with spectral and cut guarantees. Journal of Machine Learning Research, Vol. 20, 116 (2019), 1-42.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 28th International Conference on International Conference on Machine Learning","author":"Nickel Maximilian","year":"2011","unstructured":"Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A three-way model for collective learning on multi-relational data. In Proceedings of the 28th International Conference on International Conference on Machine Learning (Bellevue, Washington, USA) (ICML'11). Omnipress, Madison, WI, USA, 809\u2013816."},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the 16th International Conference on Information Fusion. IEEE, 899-905","author":"Papalexakis Evangelos E","year":"2013","unstructured":"Evangelos E Papalexakis, Leman Akoglu, and Dino Ience. 2013. Do more views of a graph help? community detection and clustering in multi-graphs. In Proceedings of the 16th International Conference on Information Fusion. IEEE, 899-905."},{"key":"e_1_3_2_1_40_1","volume-title":"Introduction to tensor decompositions and their applications in machine learning. arXiv preprint arXiv:1711.10781","author":"Rabanser Stephan","year":"2017","unstructured":"Stephan Rabanser, Oleksandr Shchur, and Stephan G\u00fcnnemann. 2017. Introduction to tensor decompositions and their applications in machine learning. arXiv preprint arXiv:1711.10781 (2017)."},{"key":"e_1_3_2_1_41_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations.","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_42_1","volume-title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations.","author":"Sener Ozan","year":"2018","unstructured":"Ozan Sener and Silvio Savarese. 2018. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2017.2690524"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102712"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.02.035"},{"key":"e_1_3_2_1_48_1","volume-title":"Effective Data Augmentation With Diffusion Models. In The Twelfth International Conference on Learning Representations.","author":"Trabucco Brandon","year":"2024","unstructured":"Brandon Trabucco, Kyle Doherty, Max A Gurinas, and Ruslan Salakhutdinov. 2024. Effective Data Augmentation With Diffusion Models. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645694"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"e_1_3_2_1_51_1","first-page":"6861","article-title":"Simplifying graph convolutional networks","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR, 6861-6871.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD '22)","author":"Wu Zhebin","year":"2022","unstructured":"Zhebin Wu, Lin Shu, Ziyue Xu, Yaomin Chang, Chuan Chen, and Zibin Zheng. 2022. Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD '22). Association for Computing Machinery, New York, NY, USA, 2090\u20132099."},{"key":"e_1_3_2_1_53_1","volume-title":"Proceedings of the ACM on Web Conference 2025 (Sydney NSW, Australia) (WWW '25)","author":"Xiao Zhenbang","year":"2025","unstructured":"Zhenbang Xiao, Yu Wang, Shunyu Liu, Bingde Hu, Huiqiong Wang, Mingli Song, and Tongya Zheng. 2025. Disentangled Condensation for Large-scale Graphs. In Proceedings of the ACM on Web Conference 2025 (Sydney NSW, Australia) (WWW '25). Association for Computing Machinery, New York, NY, USA, 4494\u20134506."},{"key":"e_1_3_2_1_54_1","volume-title":"Machine Learning and Knowledge Discovery in Databases. Research Track, Albert Bifet, Jesse Davis, Tomas Krilavi\u010dius, Meelis Kull, Eirini Ntoutsi, and Indr\u0117 \u017dliobait\u0117 (Eds.)","author":"Xiao Zhenbang","unstructured":"Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, and Tongya Zheng. 2024. Simple Graph Condensation. In Machine Learning and Knowledge Discovery in Databases. Research Track, Albert Bifet, Jesse Davis, Tomas Krilavi\u010dius, Meelis Kull, Eirini Ntoutsi, and Indr\u0117 \u017dliobait\u0117 (Eds.). Springer Nature Switzerland, Cham, 53-71."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860485"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599398"},{"key":"e_1_3_2_1_57_1","unstructured":"Beining Yang Kai Wang Qingyun Sun Cheng Ji Xingcheng Fu Hao Tang Yang You and Jianxin Li. 2023. Does Graph Distillation See Like Vision Dataset Counterpart?. In NeurIPS."},{"key":"e_1_3_2_1_58_1","volume-title":"Graph contrastive learning with augmentations. Advances in neural information processing systems","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems, Vol. 33 (2020), 5812-5823."},{"key":"e_1_3_2_1_59_1","unstructured":"Hanqing Zeng Hongkuan Zhou Ajitesh Srivastava Rajgopal Kannan and Viktor Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In ICLR."},{"key":"e_1_3_2_1_60_1","volume-title":"ICML 2024","author":"Zhang Yuchen","year":"2024","unstructured":"Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, and Yang You. 2024. Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching. ICML 2024 (2024)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"e_1_3_2_1_62_1","volume-title":"Dataset Condensation with Gradient Matching. In International Conference on Learning Representations.","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021b. Dataset Condensation with Gradient Matching. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_1_64_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems.","author":"Zheng Xin","year":"2023","unstructured":"Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan. 2023. Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_65_1","volume-title":"Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in neural information processing systems","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in neural information processing systems, Vol. 33 (2020), 7793-7804."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07796-z"}],"event":{"name":"WSDM '26:The Nineteenth ACM International Conference on Web Search and Data Mining","location":"Boise ID USA","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:59:12Z","timestamp":1771264752000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3773966.3777968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":66,"alternative-id":["10.1145\/3773966.3777968","10.1145\/3773966"],"URL":"https:\/\/doi.org\/10.1145\/3773966.3777968","relation":{},"subject":[],"published":{"date-parts":[[2026,2,21]]},"assertion":[{"value":"2026-02-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}