{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:14:48Z","timestamp":1774718088632,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"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":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614871","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"2432-2441","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Explainable Spatio-Temporal Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7002-3585","authenticated-orcid":false,"given":"Jiabin","family":"Tang","sequence":"first","affiliation":[{"name":"University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-2211","authenticated-orcid":false,"given":"Lianghao","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Alexander A. Alemi Ian Fischer Joshua V. Dillon and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In ICLR (Poster). Alexander A. Alemi Ian Fischer Joshua V. Dillon and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In ICLR (Poster)."},{"key":"e_1_3_2_1_2_1","unstructured":"Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS. Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS."},{"key":"e_1_3_2_1_3_1","unstructured":"Defu Cao Yujing Wang Juanyong Duan Ce Zhang Xia Zhu Congrui Huang Yunhai Tong Bixiong Xu etal 2021. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. CoRR Vol. abs\/2103.07719 (2021). Defu Cao Yujing Wang Juanyong Duan Ce Zhang Xia Zhu Congrui Huang Yunhai Tong Bixiong Xu et al. 2021. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. CoRR Vol. abs\/2103.07719 (2021)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_5_1","first-page":"1684","article-title":"Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting","volume":"139","author":"Yuzhou Chen","year":"2021","unstructured":"Yuzhou Chen et al. 2021 . Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting . In ICML , Vol. 139. 1684 -- 1694 . Yuzhou Chen et al. 2021. Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting. In ICML, Vol. 139. 1684--1694.","journal-title":"ICML"},{"key":"e_1_3_2_1_6_1","volume-title":"Gel","author":"Chen Yuzhou","year":"2022","unstructured":"Yuzhou Chen , Ignacio Segovia-Dominguez , Baris Coskunuzer , and Yulia R . Gel . 2022 . TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. In ICLR. Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, and Yulia R. Gel. 2022. TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. In ICLR."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Jeongwhan Choi Hwangyong Choi etal 2022. Graph Neural Controlled Differential Equations for Traffic Forecasting. In AAAI. 6367--6374. Jeongwhan Choi Hwangyong Choi et al. 2022. Graph Neural Controlled Differential Equations for Traffic Forecasting. In AAAI. 6367--6374.","DOI":"10.1609\/aaai.v36i6.20587"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Zheng Fang Qingqing Long etal 2021. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. In KDD. ACM 364--373. Zheng Fang Qingqing Long et al. 2021. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. In KDD. ACM 364--373.","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Xu Geng Yaguang Li Leye Wang Lingyu Zhang Qiang Yang Jieping Ye and Yan Liu. 2019. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. In AAAI. 3656--3663. Xu Geng Yaguang Li Leye Wang Lingyu Zhang Qiang Yang Jieping Ye and Yan Liu. 2019. Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting. In AAAI. 3656--3663.","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Shengnan Guo Youfang Lin Ning Feng Chao Song and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI. 922--929. Shengnan Guo Youfang Lin Ning Feng Chao Song and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In AAAI. 922--929.","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Liangzhe Han Bowen Du Leilei Sun Yanjie Fu Yisheng Lv and Hui Xiong. 2021. Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. In KDD. ACM 547--555. Liangzhe Han Bowen Du Leilei Sun Yanjie Fu Yisheng Lv and Hui Xiong. 2021. Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting. In KDD. ACM 547--555.","DOI":"10.1145\/3447548.3467275"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Chao Huang Junbo Zhang Yu Zheng etal 2018. DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. In CIKM. ACM 1423--1432. Chao Huang Junbo Zhang Yu Zheng et al. 2018. DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. In CIKM. ACM 1423--1432.","DOI":"10.1145\/3269206.3271793"},{"key":"e_1_3_2_1_13_1","volume-title":"Breakthroughs in statistics","author":"Huber Peter J","unstructured":"Peter J Huber . 1992. Robust estimation of a location parameter . In Breakthroughs in statistics . Springer , 492--518. Peter J Huber. 1992. Robust estimation of a location parameter. In Breakthroughs in statistics. Springer, 492--518."},{"key":"e_1_3_2_1_14_1","unstructured":"Eric Jang Shixiang Gu and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In ICLR. Eric Jang Shixiang Gu and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In ICLR."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Renhe Jiang Du Yin Zhaonan Wang Yizhuo Wang Jiewen Deng Hangchen Liu Zekun Cai Jinliang Deng Xuan Song and Ryosuke Shibasaki. 2021. DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction. In CIKM. ACM 4515--4525. Renhe Jiang Du Yin Zhaonan Wang Yizhuo Wang Jiewen Deng Hangchen Liu Zekun Cai Jinliang Deng Xuan Song and Ryosuke Shibasaki. 2021. DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction. In CIKM. ACM 4515--4525.","DOI":"10.1145\/3459637.3482000"},{"key":"e_1_3_2_1_16_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster) . Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster)."},{"key":"e_1_3_2_1_17_1","volume-title":"ICML","volume":"162","author":"Lan Shiyong","year":"2022","unstructured":"Shiyong Lan , Yitong Ma , Weikang Huang , Wenwu Wang , Hongyu Yang , and Pyang Li . 2022 . DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting . In ICML , Vol. 162 . PMLR, 11906--11917. Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. 2022. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. In ICML, Vol. 162. PMLR, 11906--11917."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Mengzhang Li and Zhanxing Zhu. 2021. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. In AAAI. 4189--4196. Mengzhang Li and Zhanxing Zhu. 2021. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. In AAAI. 4189--4196.","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"e_1_3_2_1_19_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR (Poster). Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR (Poster)."},{"key":"e_1_3_2_1_20_1","volume-title":"Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. arXiv preprint arXiv:2204.08587","author":"Li Zhonghang","year":"2022","unstructured":"Zhonghang Li , Chao Huang , Lianghao Xia , Yong Xu , and Jian Pei . 2022. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. arXiv preprint arXiv:2204.08587 ( 2022 ). Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, and Jian Pei. 2022. Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction. arXiv preprint arXiv:2204.08587 (2022)."},{"key":"e_1_3_2_1_21_1","volume-title":"MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. In KDD. ACM, 1042--1050.","author":"Liu Dachuan","year":"2022","unstructured":"Dachuan Liu , Jin Wang , 2022 . MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. In KDD. ACM, 1042--1050. Dachuan Liu, Jin Wang, et al. 2022. MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting. In KDD. ACM, 1042--1050."},{"key":"e_1_3_2_1_22_1","unstructured":"Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong etal 2020. Parameterized Explainer for Graph Neural Network. In NeurIPS. Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong et al. 2020. Parameterized Explainer for Graph Neural Network. In NeurIPS."},{"key":"e_1_3_2_1_23_1","unstructured":"Chris J. Maddison Andriy Mnih and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In ICLR. Chris J. Maddison Andriy Mnih and Yee Whye Teh. 2017. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. In ICLR."},{"key":"e_1_3_2_1_24_1","volume-title":"ICML","volume":"162","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao , Mia Liu , and Pan Li . 2022 . Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism . In ICML , Vol. 162 . PMLR, 15524--15543. Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism. In ICML, Vol. 162. PMLR, 15524--15543."},{"key":"e_1_3_2_1_25_1","volume-title":"Utilizing Real-World Transportation Data for Accurate Traffic Prediction","author":"Pan Bei","unstructured":"Bei Pan , Ugur Demiryurek , and Cyrus Shahabi . 2012. Utilizing Real-World Transportation Data for Accurate Traffic Prediction . In ICDM. IEEE Computer Society , 595--604. Bei Pan, Ugur Demiryurek, and Cyrus Shahabi. 2012. Utilizing Real-World Transportation Data for Accurate Traffic Prediction. In ICDM. IEEE Computer Society, 595--604."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. In KDD. ACM 1720--1730. Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. In KDD. ACM 1720--1730.","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_3_2_1_27_1","volume-title":"FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting. In IJCAI. ijcai.org, 3926--3932.","author":"Rao Xuan","year":"2022","unstructured":"Xuan Rao , Hao Wang , Liang Zhang , Jing Li , Shuo Shang , and Peng Han . 2022 . FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting. In IJCAI. ijcai.org, 3926--3932. Xuan Rao, Hao Wang, Liang Zhang, Jing Li, Shuo Shang, and Peng Han. 2022. FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting. In IJCAI. ijcai.org, 3926--3932."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Marco T\u00fa lio Ribeiro Sameer Singh etal 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD. ACM 1135--1144. Marco T\u00fa lio Ribeiro Sameer Singh et al. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In KDD. ACM 1135--1144.","DOI":"10.18653\/v1\/N16-3020"},{"key":"e_1_3_2_1_29_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 ICLR. Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov. 2021. Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. In ICLR."},{"key":"e_1_3_2_1_30_1","unstructured":"Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LS\u2122 Network: A Machine Learning Approach for Precipitation Nowcasting. In NIPS. 802--810. Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LS\u2122 Network: A Machine Learning Approach for Precipitation Nowcasting. In NIPS. 802--810."},{"key":"e_1_3_2_1_31_1","volume-title":"Opening the Black Box of Deep Neural Networks via Information. CoRR","author":"Shwartz-Ziv Ravid","year":"2017","unstructured":"Ravid Shwartz-Ziv and Naftali Tishby . 2017. Opening the Black Box of Deep Neural Networks via Information. CoRR , Vol. abs\/ 1703 .00810 ( 2017 ). Ravid Shwartz-Ziv and Naftali Tishby. 2017. Opening the Black Box of Deep Neural Networks via Information. CoRR, Vol. abs\/1703.00810 (2017)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Chao Song Youfang Lin Shengnan Guo and Huaiyu Wan. 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI. 914--921. Chao Song Youfang Lin Shengnan Guo and Huaiyu Wan. 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. In AAAI. 914--921.","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Yongduo Sui Xiang Wang Jiancan Wu etal 2022. Causal Attention for Interpretable and Generalizable Graph Classification. In KDD. ACM 1696--1705. Yongduo Sui Xiang Wang Jiancan Wu et al. 2022. Causal Attention for Interpretable and Generalizable Graph Classification. In KDD. ACM 1696--1705.","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_1_34_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR (Poster). Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR (Poster)."},{"key":"e_1_3_2_1_35_1","volume-title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR","author":"Wu Bingzhe","year":"2022","unstructured":"Bingzhe Wu , Jintang Li , Junchi Yu , Yatao Bian , Hengtong Zhang , Chaochao Chen , Chengbin Hou , Guoji Fu , Liang Chen , Tingyang Xu , Yu Rong , Xiaolin Zheng , Junzhou Huang , Ran He , Baoyuan Wu , Guangyu Sun , Peng Cui , Zibin Zheng , Zhe Liu , and Peilin Zhao . 2022a. A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR , Vol. abs\/ 2205 .10014 ( 2022 ). Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, and Peilin Zhao. 2022a. A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR, Vol. abs\/2205.10014 (2022)."},{"key":"e_1_3_2_1_36_1","unstructured":"Tailin Wu Hongyu Ren Pan Li and Jure Leskovec. 2020c. Graph Information Bottleneck. In NeurIPS. Tailin Wu Hongyu Ren Pan Li and Jure Leskovec. 2020c. Graph Information Bottleneck. In NeurIPS."},{"key":"e_1_3_2_1_37_1","volume-title":"Chawla","author":"Wu Xian","year":"2020","unstructured":"Xian Wu , Chao Huang , Chuxu Zhang , and Nitesh V . Chawla . 2020 a. Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. In WWW. ACM \/ IW 3C2, 2320--2330. Xian Wu, Chao Huang, Chuxu Zhang, and Nitesh V. Chawla. 2020a. Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. In WWW. ACM \/ IW3C2, 2320--2330."},{"key":"e_1_3_2_1_38_1","unstructured":"Yingxin Wu Xiang Wang An Zhang Xiangnan He and Tat-Seng Chua. 2022b. Discovering Invariant Rationales for Graph Neural Networks. In ICLR. Yingxin Wu Xiang Wang An Zhang Xiangnan He and Tat-Seng Chua. 2022b. Discovering Invariant Rationales for Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Zonghan Wu Shirui Pan Guodong Long etal 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In IJCAI. 1907--1913. Zonghan Wu Shirui Pan Guodong Long et al. 2019. Graph WaveNet for Deep Spatial-Temporal Graph Modeling. In IJCAI. 1907--1913.","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_3_2_1_40_1","unstructured":"Zonghan Wu Shirui Pan Guodong Long Jing Jiang Xiaojun Chang and Chengqi Zhang. 2020b. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD. ACM 753--763. Zonghan Wu Shirui Pan Guodong Long Jing Jiang Xiaojun Chang and Chengqi Zhang. 2020b. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD. ACM 753--763."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Lianghao Xia Chao Huang Yong Xu Peng Dai Liefeng Bo etal 2021. Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. In IJCAI. ijcai.org 1631--1637. Lianghao Xia Chao Huang Yong Xu Peng Dai Liefeng Bo et al. 2021. Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning. In IJCAI. ijcai.org 1631--1637.","DOI":"10.24963\/ijcai.2021\/225"},{"key":"e_1_3_2_1_42_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR. Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Huaxiu Yao Xianfeng Tang Hua Wei Guanjie Zheng and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In AAAI. 5668--5675. Huaxiu Yao Xianfeng Tang Hua Wei Guanjie Zheng and Zhenhui Li. 2019. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. In AAAI. 5668--5675.","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"e_1_3_2_1_44_1","unstructured":"Huaxiu Yao Fei Wu Jintao Ke Xianfeng Tang Yitian Jia Siyu Lu Pinghua Gong Jieping Ye and Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI. 2588--2595. Huaxiu Yao Fei Wu Jintao Ke Xianfeng Tang Yitian Jia Siyu Lu Pinghua Gong Jieping Ye and Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In AAAI. 2588--2595."},{"key":"e_1_3_2_1_45_1","unstructured":"Zhitao Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik and Jure Leskovec. 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. In NeurIPS. 9240--9251. Zhitao Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik and Jure Leskovec. 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. In NeurIPS. 9240--9251."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI. ijcai.org 3634--3640. Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI. ijcai.org 3634--3640.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_47_1","unstructured":"Junchi Yu Tingyang Xu Yu Rong Yatao Bian Junzhou Huang and Ran He. 2021. Graph Information Bottleneck for Subgraph Recognition. In ICLR. Junchi Yu Tingyang Xu Yu Rong Yatao Bian Junzhou Huang and Ran He. 2021. Graph Information Bottleneck for Subgraph Recognition. In ICLR."},{"key":"e_1_3_2_1_48_1","volume-title":"XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. ACM, 430--438.","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan , Jiliang Tang , Xia Hu , and Shuiwang Ji . 2020 a. XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. ACM, 430--438. Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020a. XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. ACM, 430--438."},{"key":"e_1_3_2_1_49_1","volume-title":"Explainability in Graph Neural Networks: A Taxonomic Survey. CoRR","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan , Haiyang Yu , Shurui Gui , and Shuiwang Ji. 2020b. Explainability in Graph Neural Networks: A Taxonomic Survey. CoRR , Vol. abs\/ 2012 .15445 ( 2020 ). Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020b. Explainability in Graph Neural Networks: A Taxonomic Survey. CoRR, Vol. abs\/2012.15445 (2020)."},{"key":"e_1_3_2_1_50_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"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 ICML (Proceedings of Machine Learning Research , Vol. 139). PMLR, 12241-- 12252 . Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021. On Explainability of Graph Neural Networks via Subgraph Explorations. In ICML (Proceedings of Machine Learning Research, Vol. 139). PMLR, 12241--12252."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Junbo Zhang Yu Zheng etal 2017b. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI. 1655--1661. Junbo Zhang Yu Zheng et al. 2017b. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI. 1655--1661.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Junbo Zhang Yu Zheng and Dekang Qi. 2017a. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI. 1655--1661. Junbo Zhang Yu Zheng and Dekang Qi. 2017a. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI. 1655--1661.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Qianru Zhang Chao Huang Lianghao Xia Zheng Wang Zhonghang Li and Siuming Yiu. 2023 a. Automated Spatio-Temporal Graph Contrastive Learning. In WWW. 295--305. Qianru Zhang Chao Huang Lianghao Xia Zheng Wang Zhonghang Li and Siuming Yiu. 2023 a. Automated Spatio-Temporal Graph Contrastive Learning. In WWW. 295--305.","DOI":"10.1145\/3543507.3583304"},{"key":"e_1_3_2_1_54_1","unstructured":"Qianru Zhang Chao Huang Lianghao Xia Zheng Wang Siu Ming Yiu and Ruihua Han. 2023 b. Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation. In ICML. PMLR 41151--41163. Qianru Zhang Chao Huang Lianghao Xia Zheng Wang Siu Ming Yiu and Ruihua Han. 2023 b. Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation. In ICML. PMLR 41151--41163."},{"key":"e_1_3_2_1_55_1","volume-title":"GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234--1241.","author":"Zheng Chuanpan","year":"2020","unstructured":"Chuanpan Zheng , Xiaoliang Fan , Cheng Wang , and Jianzhong Qi . 2020 . GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234--1241. Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. GMAN: A Graph Multi-Attention Network for Traffic Prediction. In AAAI. 1234--1241."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614871","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:57Z","timestamp":1750178217000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":55,"alternative-id":["10.1145\/3583780.3614871","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614871","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}