{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:51:48Z","timestamp":1765504308119,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","funder":[{"name":"Korean Ministry of Environment","award":["2021003310005"],"award-info":[{"award-number":["2021003310005"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761376","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:29:28Z","timestamp":1762561768000},"page":"3323-3332","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CHEM: Causally and Hierarchically Explaining Molecules"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7844-0548","authenticated-orcid":false,"given":"Gyeongdong","family":"Woo","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9385-5295","authenticated-orcid":false,"given":"Soyoung","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7432-2975","authenticated-orcid":false,"given":"Donghyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Environmental Engineering, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9689-5890","authenticated-orcid":false,"given":"Kimoon","family":"Na","sequence":"additional","affiliation":[{"name":"School of Environmental Engineering, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9451-4290","authenticated-orcid":false,"given":"Changhyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3393-7505","authenticated-orcid":false,"given":"Jinhee","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Environmental Engineering, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1423-4292","authenticated-orcid":false,"given":"Jong-June","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Seoul, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"22131","article-title":"Learning causally invariant representations for out-of-distribution generalization on graphs","volume":"35","author":"Chen Yongqiang","year":"2022","unstructured":"Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, MA Kaili, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng. 2022. Learning causally invariant representations for out-of-distribution generalization on graphs. Advances in Neural Information Processing Systems, Vol. 35 (2022), 22131-22148.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_2_1","first-page":"1561","article-title":"Equivariant graph neural networks for toxicity prediction","volume":"36","author":"Cremer Julian","year":"2023","unstructured":"Julian Cremer, Leonardo Medrano Sandonas, Alexandre Tkatchenko, Djork-Arn\u00e9 Clevert, and Gianni De Fabritiis. 2023. Equivariant graph neural networks for toxicity prediction. Chemical Research in Toxicology, Vol. 36, 10 (2023), 1561-1573.","journal-title":"Chemical Research in Toxicology"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.200800178"},{"key":"e_1_3_2_1_5_1","first-page":"24934","article-title":"Debiasing graph neural networks via learning disentangled causal substructure","volume":"35","author":"Fan Shaohua","year":"2022","unstructured":"Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, and Jian Tang. 2022. Debiasing graph neural networks via learning disentangled causal substructure. Advances in Neural Information Processing Systems, Vol. 35 (2022), 24934-24946.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","unstructured":"National Center for Advancing Translational Sciences (NCATS). Accessed 2024. Tox21 Challenge. https:\/\/tripod.nih.gov\/tox21\/challenge\/ Data retrieved from the official Tox21 Challenge website hosted by NIH.."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad305"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i16.33891"},{"key":"e_1_3_2_1_9_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_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i11.29157"},{"key":"e_1_3_2_1_11_1","volume-title":"Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkE3y85ee","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkE3y85ee"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00798"},{"key":"e_1_3_2_1_13_1","volume-title":"A survey on explainability of graph neural networks","author":"Kakkad Jaykumar","year":"2023","unstructured":"Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, and Sourav Medya. 2023. A survey on explainability of graph neural networks. IEEE Data Engineering Bulletin, Vol. 47, 2 (2023)."},{"key":"e_1_3_2_1_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. ICLR","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599437"},{"key":"e_1_3_2_1_16_1","volume-title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1jE5L5gl","author":"Maddison Chris J.","year":"2017","unstructured":"Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1jE5L5gl"},{"key":"e_1_3_2_1_17_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_1_18_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrestox.2c00384"},{"key":"e_1_3_2_1_19_1","volume-title":"Tudataset: A collection of benchmark datasets for learning with graphs. ICMLW","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020. Tudataset: A collection of benchmark datasets for learning with graphs. ICMLW (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_1_21_1","volume-title":"Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics","author":"Sun Mengying","year":"2020","unstructured":"Mengying Sun, Sendong Zhao, Coryandar Gilvary, Olivier Elemento, Jiayu Zhou, and Fei Wang. 2020. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics, Vol. 21, 3 (2020), 919-935."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"e_1_3_2_1_23_1","volume-title":"Graph attention networks. ICLR","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR (2018)."},{"key":"e_1_3_2_1_24_1","volume-title":"Motif Masking-based Self-Supervised Learning For Molecule Graph Representation Learning. In 2023 IEEE International Conference on e-Business Engineering (ICEBE). IEEE, 114-121","author":"Wu Yasu","year":"2023","unstructured":"Yasu Wu, Changlong Fu, Manwen Yang, Haoran Duan, and Cheng Xie. 2023. Motif Masking-based Self-Supervised Learning For Molecule Graph Representation Learning. In 2023 IEEE International Conference on e-Business Engineering (ICEBE). IEEE, 114-121."},{"key":"e_1_3_2_1_25_1","volume-title":"Discovering Invariant Rationales for Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hGXij5rfiHw","author":"Wu Yingxin","year":"2022","unstructured":"Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022. Discovering Invariant Rationales for Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hGXij5rfiHw"},{"key":"e_1_3_2_1_26_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513-530."},{"key":"e_1_3_2_1_27_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_1_28_1","first-page":"12964","article-title":"Learning substructure invariance for out-of-distribution molecular representations","volume":"35","author":"Yang Nianzu","year":"2022","unstructured":"Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, and Junchi Yan. 2022. Learning substructure invariance for out-of-distribution molecular representations. Advances in Neural Information Processing Systems, Vol. 35 (2022), 12964-12978.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_29_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_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01879"},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Machine Learning. PMLR, 25581-25594","author":"Yu Zhaoning","year":"2022","unstructured":"Zhaoning Yu and Hongyang Gao. 2022a. Molecular representation learning via heterogeneous motif graph neural networks. In International Conference on Machine Learning. PMLR, 25581-25594."},{"key":"e_1_3_2_1_32_1","volume-title":"Motifexplainer: a motif-based graph neural network explainer. arXiv preprint arXiv:2202.00519","author":"Yu Zhaoning","year":"2022","unstructured":"Zhaoning Yu and Hongyang Gao. 2022b. Motifexplainer: a motif-based graph neural network explainer. arXiv preprint arXiv:2202.00519 (2022)."},{"key":"e_1_3_2_1_33_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, Vol. 45, 5 (2022), 5782-5799."},{"key":"e_1_3_2_1_34_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_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-023-00825-5"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3372775"},{"key":"e_1_3_2_1_37_1","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021. Motif-based graph self-supervised learning for molecular property prediction. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15870-15882.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29648"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:49:56Z","timestamp":1765504196000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":38,"alternative-id":["10.1145\/3746252.3761376","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761376","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}