{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:33:34Z","timestamp":1777613614919,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645674","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"992-1002","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Globally Interpretable Graph Learning via Distribution Matching"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4387-5574","authenticated-orcid":false,"given":"Yi","family":"Nian","sequence":"first","affiliation":[{"name":"University of Chicago, Chicago, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0204-021X","authenticated-orcid":false,"given":"Yurui","family":"Chang","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5054-954X","authenticated-orcid":false,"given":"Wei","family":"Jin","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2539-3352","authenticated-orcid":false,"given":"Lu","family":"Lin","sequence":"additional","affiliation":[{"name":"Pennsylvania State University, State College, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-01974-x"},{"key":"e_1_3_2_2_2_1","volume-title":"Global explainability of gnns via logic combination of learned concepts. arXiv preprint arXiv:2210.07147","author":"Azzolin Steve","year":"2022","unstructured":"Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Li\u00f2 , and Andrea Passerini. 2022. Global explainability of gnns via logic combination of learned concepts. arXiv preprint arXiv:2210.07147 (2022)."},{"key":"e_1_3_2_2_3_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_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188410"},{"key":"e_1_3_2_2_7_1","volume-title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation. arXiv preprint arXiv:2402.03358","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. arXiv preprint arXiv:2402.03358 (2024)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3187455"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"e_1_3_2_2_10_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_2_11_1","volume-title":"International Conference on Machine Learning. https:\/\/api.semanticscholar.org\/CorpusID:249192018","author":"Kim Jang-Hyun","year":"2022","unstructured":"Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song. 2022. Dataset Condensation via Efficient Synthetic-Data Parameterization. In International Conference on Machine Learning. https:\/\/api.semanticscholar.org\/CorpusID:249192018"},{"key":"e_1_3_2_2_12_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_13_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_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498499"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539435"},{"key":"e_1_3_2_2_16_1","volume-title":"Spectral Augmentation for Self-Supervised Learning on Graphs. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=DjzBCrMBJ_p","author":"Lin Lu","year":"2023","unstructured":"Lu Lin, Jinghui Chen, and Hongning Wang. 2023. Spectral Augmentation for Self-Supervised Learning on Graphs. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=DjzBCrMBJ_p"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403233"},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Machine Learning. PMLR, 22028--22041","author":"Liu Songtao","year":"2023","unstructured":"Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, and Dinghao Wu. 2023. FusionRetro: molecule representation fusion via in-context learning for retrosynthetic planning. In International Conference on Machine Learning. PMLR, 22028--22041."},{"key":"e_1_3_2_2_19_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. 2020a. Parameterized explainer for graph neural network. Advances in neural information processing systems , Vol. 33 (2020), 19620--19631."},{"key":"e_1_3_2_2_20_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. 2020b. Parameterized explainer for graph neural network. Advances in neural information processing systems , Vol. 33 (2020), 19620--19631."},{"key":"e_1_3_2_2_21_1","volume-title":"The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712","author":"Maddison Chris J","year":"2016","unstructured":"Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 (2016)."},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Machine Learning. PMLR, 15524--15543","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and generalizable graph learning via stochastic attention mechanism. In International Conference on Machine Learning. PMLR, 15524--15543."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"e_1_3_2_2_24_1","volume-title":"Higher-order explanations of graph neural networks via relevant walks","author":"Schnake Thomas","year":"2021","unstructured":"Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Sch\u00fctt, Klaus-Robert M\u00fcller, and Gr\u00e9goire Montavon. 2021. Higher-order explanations of graph neural networks via relevant walks. IEEE transactions on pattern analysis and machine intelligence, Vol. 44, 11 (2021), 7581--7596."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3115452"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D13-1170"},{"key":"e_1_3_2_2_27_1","volume-title":"Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. Advances in neural information processing systems","author":"Vu Minh","year":"2020","unstructured":"Minh Vu and My T Thai. 2020. Pgm-explainer: Probabilistic graphical model explanations for graph neural networks. Advances in neural information processing systems , Vol. 33 (2020), 12225--12235."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512189"},{"key":"e_1_3_2_2_29_1","volume-title":"GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. arXiv preprint arXiv:2209.07924","author":"Wang Xiaoqi","year":"2022","unstructured":"Xiaoqi Wang and Han-Wei Shen. 2022. GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks. arXiv preprint arXiv:2209.07924 (2022)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599398"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_32_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao 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)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403085"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/ail2.58"},{"key":"e_1_3_2_2_36_1","volume-title":"Explainability in graph neural networks: A taxonomic survey","author":"Yuan Hao","year":"2022","unstructured":"Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022. Explainability in graph neural networks: A taxonomic survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Machine Learning. PMLR, 40888--40910","author":"Zhang Hangfan","year":"2023","unstructured":"Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu. 2023. Graph contrastive backdoor attacks. In International Conference on Machine Learning. PMLR, 40888--40910."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"e_1_3_2_2_39_1","volume-title":"Konda Reddy Mopuri, and Hakan Bilen","author":"Zhao Bo","year":"2020","unstructured":"Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020. Dataset Condensation with Gradient Matching. ArXiv , Vol. abs\/2006.05929 (2020). https:\/\/api.semanticscholar.org\/CorpusID:219558792"},{"key":"e_1_3_2_2_40_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems. io","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. io"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645674","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645674","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:25:11Z","timestamp":1755822311000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":40,"alternative-id":["10.1145\/3589334.3645674","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645674","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}