{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:26:50Z","timestamp":1767706010592,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP230100899, LP210301259"],"award-info":[{"award-number":["DP230100899, LP210301259"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100000923","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\/3711896.3736983","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T20:52:41Z","timestamp":1754254361000},"page":"3586-3597","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Global Interpretable Graph-level Anomaly Detection via Prototype"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6588-3014","authenticated-orcid":false,"given":"Zhenyu","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6009-780X","authenticated-orcid":false,"given":"Ge","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-1952","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9123-5133","authenticated-orcid":false,"given":"Shan","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5494","authenticated-orcid":false,"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-5977","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3326-4147","authenticated-orcid":false,"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Sydney, New South Wales, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1","article-title":"Deep Variational Information Bottleneck","author":"Alemi Alexander A.","year":"2017","unstructured":"Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In Proc. ICLR. 1-19.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_2_1","first-page":"1","article-title":"Global Explainability of GNNs via Logic Combination of Learned Concepts","author":"Azzolin Steve","year":"2023","unstructured":"Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Lio, and Andrea Passerini. 2023. Global Explainability of GNNs via Logic Combination of Learned Concepts. In Proc. ICLR. 1-16.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_3_1","first-page":"1","article-title":"This Looks Like That","volume":"32","author":"Chen Chaofan","year":"2019","unstructured":"Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019. This Looks Like That: Deep Learning for Interpretable Image Recognition. In Proc. NeurIPS, Vol. 32. 1-12.","journal-title":"Deep Learning for Interpretable Image Recognition. In Proc. NeurIPS"},{"key":"e_1_3_2_2_4_1","first-page":"257","article-title":"Cluster-gcn","author":"Chiang Wei-Lin","year":"2019","unstructured":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-gcn: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proc. KDD. 257-266.","journal-title":"In Proc. KDD."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"e_1_3_2_2_6_1","first-page":"2224","article-title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","author":"Duvenaud David K","year":"2015","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 Proc. NeurIPS. 2224-2232.","journal-title":"Proc. NeurIPS."},{"key":"e_1_3_2_2_7_1","first-page":"1","article-title":"Learning Robust Representations via Multi-View Information Bottleneck","author":"Federici Marco","year":"2020","unstructured":"Marco Federici, Anjan Dutta, Patrick Forr\u00e9, Nate Kushman, and Zeynep Akata. 2020. Learning Robust Representations via Multi-View Information Bottleneck. In Proc. ICLR. 1-26.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_8_1","first-page":"1","article-title":"Inductive Representation Learning on Large Graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Proc. NeurIPS, Vol. 30. 1-11.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_2_9_1","first-page":"4203","article-title":"Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity","author":"Henderson Ryan","year":"2021","unstructured":"Ryan Henderson, Djork-Arn\u00e9 Clevert, and Floriane Montanari. 2021. Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity. In Proc. ICML. 4203-4213.","journal-title":"Proc. ICML."},{"key":"e_1_3_2_2_10_1","volume-title":"Proc. ICLR. 1-24","author":"Hjelm R Devon","year":"2019","unstructured":"R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2019. Learning Deep Representations by Mutual Information Estimation and Maximization. In Proc. ICLR. 1-24."},{"key":"e_1_3_2_2_11_1","first-page":"588","article-title":"Residual Correlation in Graph Neural Network Regression","author":"Jia Junteng","year":"2020","unstructured":"Junteng Jia and Austion R. Benson. 2020. Residual Correlation in Graph Neural Network Regression. In Proc. KDD. 588-598.","journal-title":"Proc. KDD."},{"key":"e_1_3_2_2_12_1","first-page":"1","article-title":"Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability","volume":"29","author":"Kim Been","year":"2016","unstructured":"Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo. 2016. Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability. In Proc. NeurIPS, Vol. 29. 1-9.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_2_13_1","first-page":"1","article-title":"Semi-Supervised Classification with Graph Convolutional Networks","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proc. ICLR. 1-14.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_14_1","first-page":"1","volume-title":"Proc. NeurIPS, H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett(Eds.)","volume":"32","author":"Knyazev Boris","year":"2019","unstructured":"Boris Knyazev, Graham W Taylor, and Mohamed Amer. 2019. Understanding Attention and Generalization in Graph Neural Networks. In Proc. NeurIPS, H. Wallach, H. Larochelle, A. Beygelzimer, F. dtextquotesingle Alch\u00e9-Buc, E. Fox, and R. Garnett(Eds.), Vol. 32. 1-11."},{"key":"e_1_3_2_2_15_1","first-page":"3308","article-title":"Explainable Classification of Brain Networks via Contrast Subgraphs","author":"Lanciano Tommaso","year":"2020","unstructured":"Tommaso Lanciano, Francesco Bonchi, and Aristides Gionis. 2020. Explainable Classification of Brain Networks via Contrast Subgraphs. In Proc. KDD. 3308-3318.","journal-title":"Proc. KDD."},{"key":"e_1_3_2_2_16_1","volume-title":"CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-Level Anomaly Detection. In Proc","author":"Li Jindong","year":"2023","unstructured":"Jindong Li, Qianli Xing, Qi Wang, and Yi Chang. 2023. CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-Level Anomaly Detection. In Proc. ECML-PKDD. Springer, 185-200."},{"key":"e_1_3_2_2_17_1","first-page":"339","article-title":"Good-d","author":"Liu Yixin","year":"2023","unstructured":"Yixin Liu, Kaize Ding, Huan Liu, and Shirui Pan. 2023a. Good-d: On Unsupervised Graph Out-of-distribution Detection. In Proc. WSDM. 339-347.","journal-title":"On Unsupervised Graph Out-of-distribution Detection. In Proc. WSDM."},{"key":"e_1_3_2_2_18_1","first-page":"8975","article-title":"Towards Self-Interpretable Graph-Level Anomaly Detection","author":"Liu Yixin","year":"2023","unstructured":"Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, and Shirui Pan. 2023b. Towards Self-Interpretable Graph-Level Anomaly Detection. In Proc. NeurIPS. 8975-8987.","journal-title":"Proc. NeurIPS."},{"key":"e_1_3_2_2_19_1","first-page":"1","volume-title":"Proc. NeurIPS, C. Cortes, N","volume":"28","author":"Lloyd James R","year":"2015","unstructured":"James R Lloyd and Zoubin Ghahramani. 2015. Statistical Model Criticism using Kernel Two Sample Tests. In Proc. NeurIPS, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett(Eds.), Vol. 28. 1-9."},{"key":"e_1_3_2_2_20_1","first-page":"19620","article-title":"Parameterized Explainer for Graph Neural Network","volume":"33","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. In Proc. NeurIPS, Vol. 33. 19620-19631.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3341802"},{"key":"e_1_3_2_2_22_1","first-page":"8170","article-title":"Graph Neural Networks for Brain Graph Learning: A Survey","author":"Luo Xuexiong","year":"2024","unstructured":"Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral, and Philip S. Yu. 2024b. Graph Neural Networks for Brain Graph Learning: A Survey. In Proc. IJCAI. 8170-8178.","journal-title":"Proc. IJCAI."},{"key":"e_1_3_2_2_23_1","first-page":"704","article-title":"Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation","author":"Ma Rongrong","year":"2022","unstructured":"Rongrong Ma, Guansong Pang, Ling Chen, and Anton van den Hengel. 2022. Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation. In Proc. WSDM. 704-714.","journal-title":"Proc. WSDM."},{"key":"e_1_3_2_2_24_1","first-page":"1","article-title":"Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping","author":"Ma Xiaoxiao","year":"2023","unstructured":"Xiaoxiao Ma, Jian Yang, Jia Wu, and Quan Z. Sheng. 2023. Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping. In Proc. KDD. 1-15.","journal-title":"Proc. KDD."},{"key":"e_1_3_2_2_25_1","first-page":"508","article-title":"On Fake News Detection with LLM Enhanced Semantics Mining","author":"Ma Xiaoxiao","year":"2024","unstructured":"Xiaoxiao Ma, Yuchen Zhang, Kaize Ding, Jian Yang, Jia Wu, and Hao Fan. 2024. On Fake News Detection with LLM Enhanced Semantics Mining. In Proc. EMNLP. 508-521.","journal-title":"Proc. EMNLP."},{"key":"e_1_3_2_2_26_1","first-page":"1","article-title":"The Concrete Distribution","author":"Maddison Chris J.","year":"2017","unstructured":"Chris J. Maddison, Andriy Mnih, Yee Whye, and Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In Proc. ICLR. 1-20.","journal-title":"A Continuous Relaxation of Discrete Random Variables. In Proc. ICLR."},{"key":"e_1_3_2_2_27_1","first-page":"1","volume-title":"Gcexplainer: Human-in-the-loop Concept-based Explanations for Graph Neural Networks. arXiv preprint arXiv:2107.11889(2021)","author":"Magister Lucie Charlotte","year":"2021","unstructured":"Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh, and Pietro Li\u00f2. 2021. Gcexplainer: Human-in-the-loop Concept-based Explanations for Graph Neural Networks. arXiv preprint arXiv:2107.11889(2021), 1-28."},{"key":"e_1_3_2_2_28_1","first-page":"15524","article-title":"Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism. In Proc. ICML. 15524-15543.","journal-title":"Proc. ICML."},{"key":"e_1_3_2_2_29_1","volume-title":"Proc. ICML Workshop on Graph Representation Learning and Beyond. 1-11","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. In Proc. ICML Workshop on Graph Representation Learning and Beyond. 1-11."},{"key":"e_1_3_2_2_30_1","first-page":"201","article-title":"Graph-level Anomaly Detection via Hierarchical Memory Networks","author":"Niu Chaoxi","year":"2023","unstructured":"Chaoxi Niu, Guansong Pang, and Ling Chen. 2023. Graph-level Anomaly Detection via Hierarchical Memory Networks. In Proc. ECML-PKDD. 201-218.","journal-title":"Proc. ECML-PKDD."},{"key":"e_1_3_2_2_31_1","first-page":"1063","article-title":"Large-scale Hierarchical Text Classification with Recursively Regularized Deep Graph-cnn","author":"Peng Hao","year":"2018","unstructured":"Hao Peng, Jianxin Li, Yu He, Yaopeng Liu, Mengjiao Bao, Lihong Wang, Yangqiu Song, and Qiang Yang. 2018. Large-scale Hierarchical Text Classification with Recursively Regularized Deep Graph-cnn. In Proc. WWW. 1063-1072.","journal-title":"Proc. WWW."},{"key":"e_1_3_2_2_32_1","first-page":"5171","article-title":"On Variational Bounds of Mutual Information","author":"Poole Ben","year":"2019","unstructured":"Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker. 2019. On Variational Bounds of Mutual Information. In Proc. ICML. 5171-5180.","journal-title":"Proc. ICML."},{"key":"e_1_3_2_2_33_1","first-page":"10772","article-title":"Explainability Methods for Graph Convolutional Neural Networks","author":"Pope Phillip E","year":"2019","unstructured":"Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019. Explainability Methods for Graph Convolutional Neural Networks. In Proc. CVPR. 10772-10781.","journal-title":"Proc. CVPR."},{"key":"e_1_3_2_2_34_1","first-page":"1","article-title":"Raising the Bar in Graph-level Anomaly Detection","author":"Qiu Chen","year":"2022","unstructured":"Chen Qiu, Marius Kloft, Stephan Mandt, and Maja Rudolph. 2022. Raising the Bar in Graph-level Anomaly Detection. In Proc. IJCAI. 1-8.","journal-title":"Proc. IJCAI."},{"key":"e_1_3_2_2_35_1","volume-title":"Nicola De Cao, and Ivan Titov","author":"Schlichtkrull Michael Sejr","year":"2021","unstructured":"Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov. 2021. Interpreting Graph Neural Networks for NLP, With Differentiable Edge Masking. In Proc. ICLR. 1-21."},{"key":"e_1_3_2_2_36_1","first-page":"1","article-title":"Interpretable Prototype-based Graph Information Bottleneck","author":"Seo Sangwoo","year":"2023","unstructured":"Sangwoo Seo, Sungwon Kim, and Chanyoung Park. 2023. Interpretable Prototype-based Graph Information Bottleneck. In Proc. NeurIPS. 1-12.","journal-title":"Proc. NeurIPS."},{"key":"e_1_3_2_2_37_1","unstructured":"Naftali Tishby Fernando C Pereira and William Bialek. 2000. The Information Bottleneck Method. arXiv preprint physics\/0004057(2000) 1-16."},{"volume-title":"Deep Learning and the Information Bottleneck Principle","author":"Tishby Naftali","key":"e_1_3_2_2_38_1","unstructured":"Naftali Tishby and Noga Zaslavsky. 2015. Deep Learning and the Information Bottleneck Principle. In IEEE information theory workshop. 1-5."},{"key":"e_1_3_2_2_39_1","first-page":"1","article-title":"Graph Attention Networks","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In Proc. ICLR. 1-12.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_40_1","first-page":"18446","article-title":"Towards Multi-grained Explainability for Graph Neural Networks","volume":"34","author":"Wang Xiang","year":"2021","unstructured":"Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, and Tat-Seng Chua. 2021. Towards Multi-grained Explainability for Graph Neural Networks. In Proc. NeurIPS, Vol. 34. 18446-18458.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_2_41_1","first-page":"7","volume-title":"Proc. NeurIPS, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin(Eds.)","volume":"33","author":"Wu Tailin","year":"2020","unstructured":"Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. 2020a. Graph Information Bottleneck. In Proc. NeurIPS, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin(Eds.), Vol. 33. 20437-20448."},{"key":"e_1_3_2_2_42_1","first-page":"8652","article-title":"Towards Global Explanations of Convolutional Neural Networks with Concept Attribution","author":"Wu Weibin","year":"2020","unstructured":"Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, Michael R Lyu, and Yu-Wing Tai. 2020b. Towards Global Explanations of Convolutional Neural Networks with Concept Attribution. In Proc. CVPR. 8652-8661.","journal-title":"Proc. CVPR."},{"key":"e_1_3_2_2_43_1","first-page":"1","article-title":"How Powerful are Graph Neural Networks?","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In Proc. ICLR. 1-17.","journal-title":"Proc. ICLR."},{"key":"e_1_3_2_2_44_1","first-page":"114","article-title":"Minimum Entropy Principle Guided Graph Neural Networks","author":"Yang Zhenyu","year":"2023","unstructured":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z Sheng, Hao Peng, Angsheng Li, Shan Xue, and Jianlin Su. 2023. Minimum Entropy Principle Guided Graph Neural Networks. In Proc. WSDM. 114-122.","journal-title":"Proc. WSDM."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3695863","article-title":". State of the Art and Potentialities of Graph-level Learning","volume":"57","author":"Yang Zhenyu","year":"2024","unstructured":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, et al., 2024. State of the Art and Potentialities of Graph-level Learning. ACM Computing Surveys, Vol. 57, 2 (2024), 1-40.","journal-title":"ACM Computing Surveys"},{"key":"e_1_3_2_2_46_1","first-page":"1","article-title":"Gnnexplainer","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. In Proc. NeurIPS. 1-12.","journal-title":"Generating Explanations for Graph Neural Networks. In Proc. NeurIPS."},{"key":"e_1_3_2_2_47_1","first-page":"430","article-title":"Xgnn","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020. Xgnn: Towards Model-level Explanations of Graph Neural Networks. In Proc. KDD. 430-438.","journal-title":"Towards Model-level Explanations of Graph Neural Networks. In Proc. KDD."},{"key":"e_1_3_2_2_48_1","first-page":"12241","article-title":"On Explainability of Graph Neural Networks via Subgraph Explorations","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 Proc. ICML. 12241-12252.","journal-title":"Proc. ICML."},{"key":"e_1_3_2_2_49_1","volume-title":"Scalable semi-supervised clustering via structural entropy with different constraints","author":"Zeng Guangjie","year":"2024","unstructured":"Guangjie Zeng, Hao Peng, Angsheng Li, Jia Wu, Chunyang Liu, and Philip S Yu. 2024. Scalable semi-supervised clustering via structural entropy with different constraints. IEEE Transactions on Knowledge and Data Engineering(2024)."},{"key":"e_1_3_2_2_50_1","first-page":"24144","article-title":"Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection","volume":"35","author":"Zhang Ge","year":"2022","unstructured":"Ge Zhang, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z Sheng, Leman Akoglu, and Charu C. Aggarwal. 2022b. Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection. In Proc. NeurIPS, Vol. 35. 24144-24157.","journal-title":"Proc. NeurIPS"},{"key":"e_1_3_2_2_51_1","first-page":"1","article-title":"An End-to-end Deep Learning Architecture for Graph Classification","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018. An End-to-end Deep Learning Architecture for Graph Classification. In Proc. AAAI. 1-8.","journal-title":"Proc. AAAI."},{"key":"e_1_3_2_2_52_1","first-page":"9127","article-title":"Protgnn","author":"Zhang Zaixi","year":"2022","unstructured":"Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Cheekong Lee. 2022a. Protgnn: Towards Self-explaining Graph Neural Networks. In Proc. AAAI. 9127-9135.","journal-title":"Towards Self-explaining Graph Neural Networks. In Proc. AAAI."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2021.0069"}],"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\/3711896.3736983","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T14:33:47Z","timestamp":1755354827000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":53,"alternative-id":["10.1145\/3711896.3736983","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736983","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"}}]}}