{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T16:47:50Z","timestamp":1755794870523,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","funder":[{"name":"Institute of Information & Communications Technology Planning & evaluation","award":["RS-2019-II190421, IITP-2025-RS-2020-II201821, RS-2024-00438686, RS-2024-00436936, IITP-2025-RS-2024-00360227, RS-2023-00225441, RS-2025-02218768"],"award-info":[{"award-number":["RS-2019-II190421, IITP-2025-RS-2020-II201821, RS-2024-00438686, RS-2024-00436936, IITP-2025-RS-2024-00360227, RS-2023-00225441, RS-2025-02218768"]}]},{"name":"Korea Creative Content Agency","award":["RS-2024-00333068"],"award-info":[{"award-number":["RS-2024-00333068"]}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00448809, NRF-2021M3H4A1A02056037"],"award-info":[{"award-number":["RS-2024-00448809, NRF-2021M3H4A1A02056037"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736995","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:30:13Z","timestamp":1754055013000},"page":"1106-1117","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing Influence Function in Explaining Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-2391","authenticated-orcid":false,"given":"Heesoo","family":"Jung","sequence":"first","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1763-8752","authenticated-orcid":false,"given":"Chanyong","family":"Kim","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8967-5255","authenticated-orcid":false,"given":"Geonhee","family":"Han","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-5806","authenticated-orcid":false,"given":"Hogun","family":"Park","sequence":"additional","affiliation":[{"name":"Sungkyunkwan University, Suwon, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00010"},{"key":"e_1_3_2_2_2_1","unstructured":"Naman Agarwal Brian Bullins and Elad Hazan. 2017. Second-order stochastic optimization for machine learning in linear time. Journal of Machine Learning Research(2017)."},{"key":"e_1_3_2_2_3_1","unstructured":"Juhan Bae Nathan Ng Alston Lo Marzyeh Ghassemi and Roger B Grosse. 2022. If influence functions are the answer then what is the question? Advances in Neural Information Processing Systems(2022)."},{"key":"e_1_3_2_2_4_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence.","author":"Bandyopadhyay Sambaran","year":"2020","unstructured":"Sambaran Bandyopadhyay and Vishal Peter. 2020. Unsupervised constrained community detection via self-expressive graph neural network. In Proceedings of the Conference on Uncertainty in Artificial Intelligence."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00045"},{"key":"e_1_3_2_2_6_1","unstructured":"Jialin Chen Shirley Wu Abhijit Gupta and Rex Ying. 2024a. D4explainer: In-distribution explanations of graph neural network via discrete denoising diffusion. Advances in Neural Information Processing Systems(2024)."},{"key":"e_1_3_2_2_7_1","volume-title":"Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks. International Conference on Machine Learning(2024)","author":"Chen Zhuomin","year":"2024","unstructured":"Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mondal, Hua Wei, and Dongsheng Luo. 2024b. Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks. International Conference on Machine Learning(2024)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/317"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"R Dennis Cook and Sanford Weisberg. 1980. Characterizations of an empirical influence function for detecting influential cases in regression. Technometrics(1980).","DOI":"10.2307\/1268187"},{"key":"e_1_3_2_2_10_1","volume-title":"Gargi Debnath, Alan J Shusterman, and Corwin Hansch.","author":"Debnath Asim Kumar","year":"1991","unstructured":"Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch. 1991. Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry(1991)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-63797-1_4"},{"key":"e_1_3_2_2_12_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","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_2_13_1","doi-asserted-by":"crossref","unstructured":"F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems(2015).","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570376"},{"key":"e_1_3_2_2_15_1","unstructured":"Statistical Analysis System Institute. 1999. SAS\/STAT user's guide. Vol. 3. SAS Publ."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Krisorn Jittorntrum. 1978. An implicit function theorem. Journal of Optimization Theory and Applications(1978).","DOI":"10.1007\/BF00933522"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714611"},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Learning Representations(2024)","author":"Kang Hyunju","year":"2024","unstructured":"Hyunju Kang, Geonhee Han, and Hogun Park. 2024. Unr-explainer: Counterfactual explanations for unsupervised node representation learning models. International Conference on Learning Representations(2024)."},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Machine Learning(2017)","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. International Conference on Machine Learning(2017)."},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning(2017)","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. International Conference on Machine Learning(2017)."},{"key":"e_1_3_2_2_21_1","unstructured":"Juanhui Li Harry Shomer Haitao Mao Shenglai Zeng Yao Ma Neil Shah Jiliang Tang and Dawei Yin. 2024. Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking. Advances in Neural Information Processing Systems(2024)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645587"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671967"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Sitao Luan Chenqing Hua Minkai Xu Qincheng Lu Jiaqi Zhu Xiao-Wen Chang Jie Fu Jure Leskovec and Doina Precup. 2024. When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability. Advances in Neural Information Processing Systems(2024).","DOI":"10.1007\/978-3-031-53468-3_4"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics.","author":"Lucic Ana","year":"2022","unstructured":"Ana Lucic, Maartje A Ter Hoeve, Gabriele Tolomei, Maarten De Rijke, and Fabrizio Silvestri. 2022. Cf-gnnexplainer: Counterfactual explanations for graph neural networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics."},{"key":"e_1_3_2_2_26_1","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(2020)."},{"key":"e_1_3_2_2_27_1","volume-title":"Clear: Generative counterfactual explanations on graphs. Advances in Neural Information Processing Systems(2022).","author":"Ma Jing","year":"2022","unstructured":"Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, and Jundong Li. 2022a. Clear: Generative counterfactual explanations on graphs. Advances in Neural Information Processing Systems(2022)."},{"key":"e_1_3_2_2_28_1","volume-title":"International Conference on Learning Representations(2022)","author":"Ma Yao","year":"2022","unstructured":"Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2022b. Is homophily a necessity for graph neural networks? International Conference on Learning Representations(2022)."},{"key":"e_1_3_2_2_29_1","volume-title":"Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks","author":"Nielsen Ian E","year":"2022","unstructured":"Ian E Nielsen, Dimah Dera, Ghulam Rasool, Ravi P Ramachandran, and Nidhal Carla Bouaynaya. 2022. Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks. IEEE Signal Processing Magazine(2022)."},{"key":"e_1_3_2_2_30_1","volume-title":"Neural Networks","volume":"164","author":"Park Hogun","year":"2023","unstructured":"Hogun Park and Jennifer Neville. 2023. Generating post-hoc explanations for Skip-gram-based node embeddings by identifying important nodes with bridgeness. Neural Networks, Vol. 164 (2023)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701551.3703515"},{"key":"e_1_3_2_2_32_1","volume-title":"Proceedings of the Conference on Uncertainty in Artificial Intelligence.","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Conference on Uncertainty in Artificial Intelligence."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20791"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Prithviraj Sen Galileo Namata Mustafa Bilgic Lise Getoor Brian Galligher and Tina Eliassi-Rad. 2008. Collective classification in network data. AI Magazine(2008) 93-93.","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_2_36_1","unstructured":"Sangwoo Seo Sungwon Kim and Chanyoung Park. 2024. Interpretable prototype-based graph information bottleneck. Advances in Neural Information Processing Systems(2024)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Charles Spearman. 1961. The proof and measurement of association between two things. (1961).","DOI":"10.1037\/11491-005"},{"key":"e_1_3_2_2_38_1","volume-title":"Explainability of Speech Recognition Transformers via Gradient-Based Attention Visualization","author":"Sun Tianli","year":"2023","unstructured":"Tianli Sun, Haonan Chen, Guosheng Hu, Lianghua He, and Cairong Zhao. 2023. Explainability of Speech Recognition Transformers via Gradient-Based Attention Visualization. IEEE Transactions on Multimedia(2023)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511948"},{"key":"e_1_3_2_2_40_1","volume-title":"Fast yet effective machine unlearning","author":"Tarun Ayush K","year":"2023","unstructured":"Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2023. Fast yet effective machine unlearning. IEEE Transactions on Neural Networks and Learning Systems(2023)."},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Learning Representations(2018)","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. International Conference on Learning Representations(2018)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599330"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_44_1","unstructured":"Boris Weisfeiler and AA Lehman. 1968. A reduction of a graph to a canonical form and an algebra arising during this reduction. In Nauchno-Technicheskaya Informatsia."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583521"},{"key":"e_1_3_2_2_46_1","unstructured":"Yaochen Xie Sumeet Katariya Xianfeng Tang Edward Huang Nikhil Rao Karthik Subbian and Shuiwang Ji. 2022. Task-agnostic graph explanations. Advances in Neural Information Processing Systems(2022)."},{"key":"e_1_3_2_2_47_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems(2019).","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(2019)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599435"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511929"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Toronto ON Canada","acronym":"KDD '25"},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736995","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:33:37Z","timestamp":1755354817000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":49,"alternative-id":["10.1145\/3711896.3736995","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736995","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}