{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:02:10Z","timestamp":1775815330664,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":74,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Joint Funds of the Zhejiang Provincial Natural Science Foundation of China","award":["LHZSD24F020001"],"award-info":[{"award-number":["LHZSD24F020001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671838","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"3277-3288","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5983-9271","authenticated-orcid":false,"given":"Yuwen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software Technology, Zhejiang University &amp; State Key Laboratory of Blockchain and Security, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0584-9129","authenticated-orcid":false,"given":"Shunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Security, Zhejiang University &amp; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-9773","authenticated-orcid":false,"given":"Tongya","family":"Zheng","sequence":"additional","affiliation":[{"name":"Big Graph Center, School of Computer and Computing Science, Hangzhou City University &amp; College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2492-5230","authenticated-orcid":false,"given":"Kaixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Security, Zhejiang University &amp; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-6048","authenticated-orcid":false,"given":"Mingli","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Security, Zhejiang University &amp; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.039"},{"key":"e_1_3_2_2_2_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Borgwardt Karsten","year":"2006","unstructured":"Karsten Borgwardt, Nicol Schraudolph, and SVN Vishwanathan. 2006. Fast computation of graph kernels. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.132"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"e_1_3_2_2_7_1","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."},{"key":"e_1_3_2_2_8_1","volume-title":"International Conference on Machine Learning.","author":"Chen Dexiong","year":"2022","unstructured":"Dexiong Chen, Leslie O'Bray, and Karsten Borgwardt. 2022. Structure-aware transformer for graph representation learning. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_9_1","volume-title":"International Joint Conference on Neural Networks.","author":"Chen Kaixuan","year":"2022","unstructured":"Kaixuan Chen, Shunyu Liu, Na Yu, Rong Yan, Quan Zhang, Jie Song, Zunlei Feng, and Mingli Song. 2022. Distribution-aware graph representation learning for transient stability assessment of power system. In International Joint Conference on Neural Networks."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Kaixuan Chen Shunyu Liu Tongtian Zhu Ji Qiao Yun Su Yingjie Tian Tongya Zheng Haofei Zhang Zunlei Feng Jingwen Ye et al. 2023. Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization. In ACM Knowledge Discovery and Data Mining.","DOI":"10.1145\/3580305.3599388"},{"key":"e_1_3_2_2_11_1","volume-title":"Powerformer: A Section-adaptive Transformer for Power Flow Adjustment. arXiv preprint arXiv:2401.02771","author":"Chen Kaixuan","year":"2024","unstructured":"Kaixuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Jie Song, and Mingli Song. 2024. Powerformer: A Section-adaptive Transformer for Power Flow Adjustment. arXiv preprint arXiv:2401.02771 (2024)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3208063"},{"key":"e_1_3_2_2_13_1","volume-title":"2022 d. FINC: An Efficient and Effective Optimization Method for Normalized Cut","author":"Chen Xiaojun","year":"2022","unstructured":"Xiaojun Chen, Zhicong Xiao, Feiping Nie, and Joshua Zhexue Huang. 2022 d. FINC: An Efficient and Effective Optimization Method for Normalized Cut. IEEE Trans on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482306"},{"key":"e_1_3_2_2_15_1","volume-title":"Towards prototype-based self-explainable graph neural network. arXiv preprint arXiv:2210.01974","author":"Dai Enyan","year":"2022","unstructured":"Enyan Dai and Suhang Wang. 2022. Towards prototype-based self-explainable graph neural network. arXiv preprint arXiv:2210.01974 (2022)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25898"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109126"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20615"},{"key":"e_1_3_2_2_20_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Feragen Aasa","year":"2013","unstructured":"Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, and Karsten Borgwardt. 2013. Scalable kernels for graphs with continuous attributes. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45167-9_11"},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Machine Learning.","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_23_1","volume-title":"Inductive Representation Learning on Large Graphs. Annual Conference on Neural Information Processing Systems.","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_24_1","volume-title":"ASGN: An active semi-supervised graph neural network for molecular property prediction. In ACM Knowledge Discovery and Data Mining.","author":"Hao Zhongkai","year":"2020","unstructured":"Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, and Cheekong Lee. 2020. ASGN: An active semi-supervised graph neural network for molecular property prediction. In ACM Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_2_25_1","unstructured":"Yongcheng Jing Yining Mao Yiding Yang Yibing Zhan Mingli Song Xinchao Wang and Dacheng Tao. 2022. Learning graph neural networks for image style transfer. In ECCV."},{"key":"e_1_3_2_2_26_1","unstructured":"Yongcheng Jing Yiding Yang Xinchao Wang Mingli Song and Dacheng Tao. 2021. Amalgamating knowledge from heterogeneous graph neural networks. In CVPR."},{"key":"e_1_3_2_2_27_1","unstructured":"Yongcheng Jing Chongbin Yuan Li Ju Yiding Yang Xinchao Wang and Dacheng Tao. 2023. Deep Graph Reprogramming. In CVPR."},{"key":"e_1_3_2_2_28_1","volume-title":"International Conference on Machine Learning.","author":"Kashima Hisashi","year":"2003","unstructured":"Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi. 2003. Marginalized kernels between labeled graphs. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_29_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_30_1","volume-title":"An introduction to case-based reasoning. Artificial intelligence review","author":"Kolodner Janet L","year":"1992","unstructured":"Janet L Kolodner. 1992. An introduction to case-based reasoning. Artificial intelligence review, Vol. 6, 1 (1992), 3--34."},{"key":"e_1_3_2_2_31_1","volume-title":"International Conference on Machine Learning.","author":"Kong Kezhi","year":"2023","unstructured":"Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C Bayan Bruss, and Tom Goldstein. 2023. GOAT: A global transformer on large-scale graphs. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00142"},{"key":"e_1_3_2_2_33_1","volume-title":"A survey of explainable graph neural networks: Taxonomy and evaluation metrics. arXiv preprint arXiv:2207.12599","author":"Li Yiqiao","year":"2022","unstructured":"Yiqiao Li, Jianlong Zhou, Sunny Verma, and Fang Chen. 2022. A survey of explainable graph neural networks: Taxonomy and evaluation metrics. arXiv preprint arXiv:2207.12599 (2022)."},{"key":"e_1_3_2_2_34_1","volume-title":"Transmission interface power flow adjustment: A deep reinforcement learning approach based on multi-task attribution map","author":"Liu Shunyu","year":"2023","unstructured":"Shunyu Liu, Wei Luo, Yanzhen Zhou, Kaixuan Chen, Quan Zhang, Huating Xu, Qinglai Guo, and Mingli Song. 2023. Transmission interface power flow adjustment: A deep reinforcement learning approach based on multi-task attribution map. IEEE Transactions on Power Systems (2023)."},{"key":"e_1_3_2_2_35_1","volume-title":"On Calibration of Graph Neural Networks for Node Classification. In International Joint Conference on Neural Networks.","author":"Liu Tong","year":"2022","unstructured":"Tong Liu, Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Hang Li, and Volker Tresp. 2022. On Calibration of Graph Neural Networks for Node Classification. In International Joint Conference on Neural Networks."},{"key":"e_1_3_2_2_36_1","first-page":"1","article-title":"Federated social recommendation with graph neural network","volume":"13","author":"Liu Zhiwei","year":"2022","unstructured":"Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu. 2022. Federated social recommendation with graph neural network. ACM Transactions on Intelligent Systems and Technology, Vol. 13, 4 (2022), 1--24.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_2_37_1","volume-title":"Annual Conference on 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. 2020. Parameterized explainer for graph neural network. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning.","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."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Yao Ming Panpan Xu Huamin Qu and Liu Ren. 2019. Interpretable and steerable sequence learning via prototypes. In ACM Knowledge Discovery and Data Mining.","DOI":"10.1145\/3292500.3330908"},{"key":"e_1_3_2_2_40_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Nikolentzos Giannis","year":"2020","unstructured":"Giannis Nikolentzos and Michalis Vazirgiannis. 2020. Random walk graph neural networks. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"e_1_3_2_2_42_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Ramp\u00e1vsek Ladislav","year":"2022","unstructured":"Ladislav Ramp\u00e1vsek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022. Recipe for a general, powerful, scalable graph transformer. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_43_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Rangapuram Syama Sundar","year":"2014","unstructured":"Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, and Matthias Hein. 2014. Tight continuous relaxation of the balanced k-cut problem. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_44_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_3_2_2_46_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Sch\u00f6lkopf Bernhard","year":"2000","unstructured":"Bernhard Sch\u00f6lkopf. 2000. The kernel trick for distances. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_47_1","volume-title":"International Conference on Computer Vision.","year":"2003","unstructured":"Shi. 2003. Multiclass spectral clustering. In International Conference on Computer Vision."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"David Silver Julian Schrittwieser Karen Simonyan Ioannis Antonoglou Aja Huang Arthur Guez Thomas Hubert Lucas Baker Matthew Lai Adrian Bolton et al. 2017. Mastering the game of go without human knowledge. nature Vol. 550 7676 (2017) 354--359.","DOI":"10.1038\/nature24270"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.070548997"},{"key":"e_1_3_2_2_51_1","unstructured":"Yongduo Sui Xiang Wang Jiancan Wu Min Lin Xiangnan He and Tat-Seng Chua. 2022. Causal attention for interpretable and generalizable graph classification. In ACM Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_2_52_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Velivckovi\u0107 Petar","year":"2018","unstructured":"Petar Velivckovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_53_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Vishwanathan SVN","year":"2006","unstructured":"SVN Vishwanathan, Karsten M Borgwardt, Nicol N Schraudolph, et al. 2006. Fast computation of graph kernels. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_54_1","volume-title":"Annual Conference on 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. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00516"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.3233\/FAIA230564"},{"key":"e_1_3_2_2_57_1","volume-title":"Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics","author":"Wang Yue","year":"2019","unstructured":"Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2019. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (2019)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3409071"},{"key":"e_1_3_2_2_59_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Wu Tailin","year":"2020","unstructured":"Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. 2020. Graph information bottleneck. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_60_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Wu Zhanghao","year":"2021","unstructured":"Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, and Ion Stoica. 2021. Representing long-range context for graph neural networks with global attention. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_61_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_2_62_1","doi-asserted-by":"crossref","unstructured":"Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In ACM Knowledge Discovery and Data Mining.","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539358"},{"key":"e_1_3_2_2_64_1","volume-title":"Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks. In Annual Conference on Neural Information Processing Systems.","author":"Yin Jun","year":"2023","unstructured":"Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, and Senzhang Wang. 2023. Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In ACM Knowledge Discovery and Data Mining.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_66_1","volume-title":"Annual Conference on 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. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_67_1","volume-title":"Annual Conference on Neural Information Processing Systems.","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_68_1","volume-title":"Graph Information Bottleneck for Subgraph Recognition. In International Conference on Learning Representations.","author":"Yu Junchi","year":"2020","unstructured":"Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He. 2020. Graph Information Bottleneck for Subgraph Recognition. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_69_1","volume-title":"Xgnn: Towards model-level explanations of graph neural networks. In ACM Knowledge Discovery and Data Mining.","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 ACM Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_2_70_1","volume-title":"International Conference on Machine Learning.","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."},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17761"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20898"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_3_2_2_74_1","volume-title":"Transition propagation graph neural networks for temporal networks","author":"Zheng Tongya","year":"2022","unstructured":"Tongya Zheng, Zunlei Feng, Tianli Zhang, Yunzhi Hao, Mingli Song, Xingen Wang, Xinyu Wang, Ji Zhao, and Chun Chen. 2022. Transition propagation graph neural networks for temporal networks. IEEE Transactions on Neural Networks and Learning Systems (2022)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671838","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":74,"alternative-id":["10.1145\/3637528.3671838","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671838","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}