{"type":"ScholarlyArticle","title":"Make Bricks with a Little Straw: Large-Scale Spatio-Temporal Graph Learning with Restricted GPU-Memory Capacity","authors":[{"name":"Binwu Wang","slug":"binwu-wang","given":"Binwu","family":"Wang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China"},{"name":"Pengkun Wang","slug":"pengkun-wang","given":"Pengkun","family":"Wang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Zhengyang Zhou","slug":"zhengyang-zhou","given":"Zhengyang","family":"Zhou","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"},{"name":"Zhe Zhao","slug":"zhe-zhao","given":"Zhe","family":"Zhao","affiliation":"University of Science and Technology of China (USTC) , Hefei , China"},{"name":"Wei Xu","slug":"wei-xu","given":"Wei","family":"Xu","affiliation":"University of Science and Technology of China (USTC) , Hefei , China"},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":"University of Science and Technology of China (USTC) , Hefei , China; Suzhou Institute for Advanced Research , USTC , Suzhou , China"}],"abstract":"Traffic prediction plays a key role in various smart city applications, which can help traffic managers make traffic plans in advance, assist online ride-hailing companies in deploying vehicles reasonably, and provide early warning of congestion for safety authorities. While increasingly complex models achieve impressive prediction performance, there are concerns about the effectiveness of these models in handling large-scale road networks. Especially for researchers who don't have access to powerful GPU devices, the expensive memory burden limits the usefulness of these models. In this paper, we take the first step of learning on the large-scale spatio-temporal graph and propose a divide-and-conquer training strategy for Large Spatio-Temporal Graph Learning, namely LarSTL. The core idea behind this strategy is to divide the large graph into multiple subgraphs, which are treated as task streams to sequentially train the model to conquer each subgraph one by one. We introduce a novel perspective based on the continuous learning paradigm to achieve this goal. In order to overcome forgetting the knowledge learned from previous subgraphs, an experience-replay strategy consolidates the learned knowledge by replaying nodes sampled from previous subgraphs. Moreover, we configure specific feature adaptors for each subgraph to extract personalized features, and it is also beneficial to consolidate the learned knowledge from the perspective of parameters. We conduct experiments using multiple large-scale traffic network datasets on a V100 GPU with only 16GB memory, and the results demonstrate that our LarSTL can achieve competitive performance and high efficiency.","doi":"10.24963/ijcai.2024/264","url":"https://ijcai.org/proceedings/2024/264","pdf":"https://ijcai.org/proceedings/2024/0264.pdf","markdown":"https://ijcai.org/proceedings/2024/264.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/264","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":264,"pages":{"first":2388,"last":2396},"keywords":["Data Mining: DM: Big data and scalability","Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. Proc. of NeurIPS, 2020.","title":"Adaptive graph convolutional recurrent network for traffic forecasting","authors":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang","year":"2020","venue":"Proc. of NeurIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2021] Yuzhou Chen, Ignacio Segovia, and Yu- lia R Gel. Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting. In Proc. of ICML, 2021.","title":"Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting","authors":"Yuzhou Chen, Ignacio Segovia, Yulia R Gel","year":"2021","venue":"Proc. of ICML","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chiang et al., 2019] Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In Proc. of KDD, 2019.","title":"Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks","authors":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh","year":"2019","venue":"Proc. of KDD","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dai et al., 2018] Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, and Le Song. Learning steady-states of it- erative algorithms over graphs. In Proc. of ICML, 2018.","title":"Learning steady-states of iterative algorithms over graphs","authors":"Dai","year":"2018","venue":"Proc. of ICML","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Du et al., 2023] Wenjie Du, Lianliang Chen, Haoran Wang, Ziyang Shan, Zhengyang Zhou, Wenwei Li, and Yang Wang. Deciphering urban traffic impacts on air quality by deep learning and emission inventory. journal of envi- ronmental sciences, 2023.","title":"Deciphering urban traffic impacts on air quality by deep learning and emission inventory","authors":"Wenjie Du, Lianliang Chen, Haoran Wang, Ziyang Shan, Zhengyang Zhou, Wenwei Li, Yang Wang","year":"2023","venue":"journal of environmental sciences","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fang et al., 2021] Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie. Spatial-temporal graph ode net- works for traffic flow forecasting. In Proc. of KDD, 2021.","title":"Spatial-temporal graph ode networks for traffic flow forecasting","authors":"Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie","year":"2021","venue":"Proc. of KDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Guo et al., 2019] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based spatial- temporal graph convolutional networks for traffic flow forecasting. In Proc. of AAAI, 2019.","title":"Attention based spatialtemporal graph convolutional networks for traffic flow forecasting","authors":"Guo","year":"2019","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guo et al., 2021] Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. Hierarchical graph convolution network for traffic forecasting. In Proc. of AAAI, 2021.","title":"Hierarchical graph convolution network for traffic forecasting","authors":"Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, Baocai Yin","year":"2021","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Guo et al., 2023] Shengnan Guo, Youfang Lin, Letian Gong, Chenyu Wang, Zeyu Zhou, Zekai Shen, Yiheng Huang, and Huaiyu Wan. Self-supervised spatial-temporal bottleneck attentive network for efficient long-term traffic forecasting. In Proc. of ICDE, 2023.","title":"Self-supervised spatial-temporal bottleneck attentive network for efficient long-term traffic forecasting","authors":"Guo","year":"2023","venue":"Proc. of ICDE","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Proc. of NeurIPS, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Proc. of NeurIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2023] Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, and Xavier Bresson. A gen- eralization of vit/mlp-mixer to graphs. In Proc. of ICML, 2023.","title":"A generalization of vit/mlp-mixer to graphs","authors":"Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann Lecun, Xavier Bresson","year":"2023","venue":"Proc. of ICML","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang et al., 2024a] Qihe Huang, Lei Shen, Ruixin Zhang, Jiahuan Cheng, Shouhong Ding, Zhengyang Zhou, and Yang Wang. Hdmixer: Hierarchical dependency with ex- tendable patch for multivariate time series forecasting. In Proc. of AAAI, 2024.","title":"Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting","authors":"Huang","year":"2024","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2024b] Qihe Huang, Lei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, and Yang Wang. Crossgnn: Confronting noisy multivariate time series via cross interaction refinement. Proc. of NeurIPS, 2024.","title":"Crossgnn: Confronting noisy multivariate time series via cross interaction refinement","authors":"Huang","year":"2024","venue":"Proc. of NeurIPS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Karypis and Kumar, 1998] George Karypis and Vipin Ku- mar. A fast and high quality multilevel scheme for parti- tioning irregular graphs. SIAM Journal on scientific Com- puting, 1998.","title":"A fast and high quality multilevel scheme for partitioning irregular graphs","authors":"Kumar Karypis","year":"1998","venue":"SIAM Journal on scientific Computing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lan et al., 2022] Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li. Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In Proc. of ICML, 2022.","title":"Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting","authors":"Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, Pyang Li","year":"2022","venue":"Proc. of ICML","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2017] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural net- work: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017.","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","authors":"Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2023] Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, 2023.","title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution","authors":"Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, Yong Li","year":"2023","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2021a] Chenxi Liu, Zhu Xiao, Dong Wang, Lei Wang, Hongbo Jiang, Hongyang Chen, and Jiangxia Yu. Exploiting spatiotemporal correlations of arrive-stay-leave behaviors for private car flow prediction. IEEE Transac- tions on Network Science and Engineering, 2021.","title":"Exploiting spatiotemporal correlations of arrive-stay-leave behaviors for private car flow prediction","authors":"Liu","year":"2021","venue":"IEEE Transactions on Network Science and Engineering","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2021b] Huihui Liu, Yiding Yang, and Xinchao Wang. Overcoming catastrophic forgetting in graph neural networks. In Proc. of AAAI, 2021.","title":"Overcoming catastrophic forgetting in graph neural networks","authors":"Liu","year":"2021","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2023a] Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, and Xuan Song. Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting. In Proc. of CIKM, 2023.","title":"Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting","authors":"Liu","year":"2023","venue":"Proc. of CIKM","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2023b] Xu Liu, Yutong Xia, Yuxuan Liang, Jun- feng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. Largest: A benchmark dataset for large-scale traffic forecasting. arXiv preprint arXiv:2306.08259, 2023.","title":"Largest: A benchmark dataset for large-scale traffic forecasting","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2024] Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, and Rui Zhao. Spatial- temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134, 2024.","title":"Spatialtemporal large language model for traffic prediction","authors":"Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, Rui Zhao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Loshchilov and Hutter, 2017] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Luxen and Vetter, 2011] Dennis Luxen and Christian Vet- ter. Real-time routing with openstreetmap data. In Pro- ceedings of the 19th ACM SIGSPATIAL International Con- ference on Advances in Geographic Information Systems, 2011.","title":"Dennis Luxen and Christian Vetter. Real-time routing with openstreetmap data","authors":"Luxen, Vetter","year":"2011","venue":"Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Miao et al., 2022a] Hao Miao, Yan Fei, Senzhang Wang, Fang Wang, and Danyan Wen. Deep learning based origin- destination prediction via contextual information fusion. Multimedia Tools and Applications, 2022.","title":"Deep learning based origin-destination prediction via contextual information fusion","authors":"Miao","year":"2022","venue":"Multimedia Tools and Applications","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Miao et al., 2022b] Hao Miao, Jiaxing Shen, Jiannong Cao, Jiangnan Xia, and Senzhang Wang. Mba-stnet: Bayes- enhanced discriminative multi-task learning for flow pre- diction. IEEE Transactions on Knowledge and Data Engi- neering, 2022.","title":"Mba-stnet: Bayesenhanced discriminative multi-task learning for flow prediction","authors":"Miao","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Miao et al., 2024] Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Zheng Kai, Feiteng Huang, Jiandong Xie, and Christian S Jensen. A unified replay-based continu- ous learning framework for spatio-temporal prediction on streaming data. In Proc. of ICDE, 2024.","title":"A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data","authors":"Hao Miao, Yan Zhao, Chenjuan Guo, Bin Yang, Zheng Kai, Feiteng Huang, Jiandong Xie, Christian S Jensen","year":"2024","venue":"Proc. of ICDE","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Qin et al., 2023] Yanjun Qin, Haiyong Luo, Fang Zhao, Yuchen Fang, Xiaoming Tao, and Chenxing Wang. Spatio- temporal hierarchical mlp network for traffic forecasting. Information Sciences, 2023.","title":"Spatiotemporal hierarchical mlp network for traffic forecasting","authors":"Yanjun Qin, Haiyong Luo, Fang Zhao, Yuchen Fang, Xiaoming Tao, Chenxing Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Shao et al., 2022a] Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu. Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing. In Proc. of CIKM, 2022.","title":"Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting","authors":"Shao","year":"2022","venue":"Proc. of CIKM","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shao et al., 2022b] Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. De- coupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112, 2022.","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","authors":"Shao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Von Luxburg, 2007] Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 2007.","title":"A tutorial on spectral clustering","authors":"Ulrike Von, Luxburg","year":"2007","venue":"Statistics and computing","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2020] Senzhang Wang, Hao Miao, Hao Chen, and Zhiqiu Huang. Multi-task adversarial spatial-temporal networks for crowd flow prediction. In CIKM, 2020.","title":"Multi-task adversarial spatial-temporal networks for crowd flow prediction","authors":"Wang","year":"2020","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2023a] Binwu Wang, Yudong Zhang, Jiahao Shi, Pengkun Wang, Xu Wang, Lei Bai, and Yang Wang. Knowledge expansion and consolidation for continual traf- fic prediction with expanding graphs. IEEE Transactions on Intelligent Transportation Systems, 2023.","title":"Knowledge expansion and consolidation for continual traffic prediction with expanding graphs","authors":"Wang","year":"2023","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2023b] Binwu Wang, Yudong Zhang, Xu Wang, Pengkun Wang, Zhengyang Zhou, Lei Bai, and Yang Wang. Pattern expansion and consolidation on evolving graphs for continual traffic prediction. In Proc. of KDD, 2023.","title":"Pattern expansion and consolidation on evolving graphs for continual traffic prediction","authors":"Wang","year":"2023","venue":"Proc. of KDD","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2023c] Chen Wang, Kaizhong Zuo, Shaokun Zhang, Hanwen Lei, Peng Hu, Zhangyi Shen, Rui Wang, and Peize Zhao. Pfnet: Large-scale traffic forecasting with progressive spatio-temporal fusion. IEEE Transactions on Intelligent Transportation Systems, 2023.","title":"Pfnet: Large-scale traffic forecasting with progressive spatio-temporal fusion","authors":"Wang","year":"2023","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2023d] Xu Wang, Pengkun Wang, Binwu Wang, Yudong Zhang, Zhengyang Zhou, Lei Bai, and Yang Wang. Latent gaussian processes based graph learn- ing for urban traffic prediction. IEEE Transactions on Ve- hicular Technology, 2023.","title":"Latent gaussian processes based graph learning for urban traffic prediction","authors":"Wang","year":"2023","venue":"IEEE Transactions on Vehicular Technology","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2023e] Yuan Wang, Zezhi Shao, Tao Sun, Chengqing Yu, Yongjun Xu, and Fei Wang. Clustering- property matters: A cluster-aware network for large scale multivariate time series forecasting. In Proc. of CIKM, 2023.","title":"Clusteringproperty matters: A cluster-aware network for large scale multivariate time series forecasting","authors":"Wang","year":"2023","venue":"Proc. of CIKM","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2023f] Zepu Wang, Yuqi Nie, Peng Sun, Nam H Nguyen, John Mulvey, and H Vincent Poor. St-mlp: A cascaded spatio-temporal linear framework with channel-independence strategy for traffic forecasting. arXiv preprint arXiv:2308.07496, 2023.","title":"St-mlp: A cascaded spatio-temporal linear framework with channel-independence strategy for traffic forecasting","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2024] Binwu Wang, Pengkun Wang, Yudong Zhang, Xu Wang, Zhengyang Zhou, Lei Bai, and Yang Wang. Towards dynamic spatial-temporal graph learning: A decoupled perspective. In Proc. of AAAI, 2024.","title":"Towards dynamic spatial-temporal graph learning: A decoupled perspective","authors":"Wang","year":"2024","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A com- prehensive survey on graph neural networks. IEEE trans- actions on neural networks and learning systems, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xia et al., 2023] Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, and Roger Zim- mermann. Deciphering spatio-temporal graph forecast- ing: A causal lens and treatment. arXiv preprint arXiv:2309.13378, 2023.","title":"Deciphering spatio-temporal graph forecasting: A causal lens and treatment","authors":"Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xu et al., 2020] Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, and Hongkai Xiong. Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908, 2020.","title":"Spatial-temporal transformer networks for traffic flow forecasting","authors":"Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Ying et al., 2018] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web- scale recommender systems. In Proc. of KDD, 2018.","title":"Graph convolutional neural networks for webscale recommender systems","authors":"Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, Jure Leskovec","year":"2018","venue":"Proc. of KDD","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2019] Chenhan Zhang, James J. Q. Yu, and Yi Liu. Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting. IEEE Access, 2019.","title":"Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting","authors":"Chenhan Zhang, J Q James, Yi Yu, Liu","year":"2019","venue":"IEEE Access","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2022] Yudong Zhang, Binwu Wang, Ziyang Shan, Zhengyang Zhou, and Yang Wang. Cmt-net: A mu- tual transition aware framework for taxicab pick-ups and drop-offs co-prediction. In Proc. of WSDM, 2022.","title":"Cmt-net: A mutual transition aware framework for taxicab pick-ups and drop-offs co-prediction","authors":"Yudong Zhang, Binwu Wang, Ziyang Shan, Zhengyang Zhou, Yang Wang","year":"2022","venue":"Proc. of WSDM","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2023] Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, and Hongwei Zhao. Mlpst: Mlp is all you need for spatio-temporal prediction. In Proc. of CIKM, 2023.","title":"Mlpst: Mlp is all you need for spatio-temporal prediction","authors":"Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S Joe Qin, Hongwei Zhao","year":"2023","venue":"Proc. of CIKM","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhou et al., 2020a] Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI open, 2020.","title":"Graph neural networks: A review of methods and applications","authors":"Zhou","year":"2020","venue":"AI open","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhou et al., 2020b] Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, and Chaochao Zhu. Foresee ur- ban sparse traffic accidents: A spatiotemporal multi- granularity perspective. IEEE TKDE, 2020.","title":"Foresee urban sparse traffic accidents: A spatiotemporal multigranularity perspective","authors":"Zhou","year":"2020","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhou et al., 2023] Zhengyang Zhou, Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, and Yang Wang. Maintaining the status quo: Cap- turing invariant relations for ood spatiotemporal learning. 2023.","title":"Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning","authors":"Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}