{"type":"ScholarlyArticle","title":"Multi-View Joint Graph Representation Learning for Urban Region Embedding","authors":[{"name":"Mingyang Zhang","slug":"mingyang-zhang","given":"Mingyang","family":"Zhang","affiliation":"The Hong Kong University of Science and Technology"},{"name":"Tong Li","slug":"tong-li","given":"Tong","family":"Li","affiliation":"The Hong Kong University of Science and Technology; University of Helsinki"},{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Tsinghua University"},{"name":"Pan Hui","slug":"pan-hui","given":"Pan","family":"Hui","affiliation":"The Hong Kong University of Science and Technology; University of Helsinki"}],"abstract":"The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view joint learning model to learn comprehensive and representative urban region embeddings. We first model different types of region correlations based on both human mobility and inherent region properties. Then, we apply a graph attention mechanism in learning region representations from each view of the built correlations. Moreover, we introduce a joint learning module that boosts the region embedding learning by sharing cross-view information and fuses multi-view embeddings by learning adaptive weights. Finally, we exploit the learned embeddings in the downstream applications of land usage classification and crime prediction in urban areas with real-world data. Extensive experiment results demonstrate that by exploiting our proposed joint learning model, the performance is improved by a large margin on both tasks compared with the state-of-the-art methods.","doi":"10.24963/ijcai.2020/611","url":"https://ijcai.org/proceedings/2020/611","pdf":"https://ijcai.org/proceedings/2020/0611.pdf","markdown":"https://ijcai.org/proceedings/2020/611.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/611","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":611,"pages":{"first":4431,"last":4437},"keywords":["Data Mining: Mining Spatial, Temporal Data","Humans and AI: Other","Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Applications"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24896/file_24896.m3u8","length_sec":299,"provider":"hls"}],"references":[{"position":1,"raw":"[Abu-El-Haija et al., 2018] Sami Abu-El-Haija, Bryan Per- ozzi, Rami Al-Rfou, and Alexander A Alemi. Watch your step: Learning node embeddings via graph attention. In Advances in Neural Information Processing Systems, pages 9180-9190, 2018.","title":"Watch your step: Learning node embeddings via graph attention","authors":"Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A Alemi","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Belkin and Niyogi, 2002] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in neural information processing systems, pages 585-591, 2002.","title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","authors":"Niyogi Belkin, Mikhail Belkin, Partha Niyogi","year":"2002","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Berg, 2007] Bruce F Berg. New York City Politics: Govern- ing Gotham. Rutgers University Press, 2007.","title":"New York City Politics: Governing Gotham","authors":"F Bruce, Berg","year":"2007","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Fu et al., 2019] Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, and Xiaolin Li. Efficient region embedding with multi-view spatial networks: A perspective of locality- constrained spatial autocorrelations. In AAAI, 2019.","title":"Efficient region embedding with multi-view spatial networks: A perspective of localityconstrained spatial autocorrelations","authors":"Yanjie Fu, Pengyang Wang, Jiadi Du, Le Wu, Xiaolin Li","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2012] Zhiwu Huang, Shiguang Shan, Haihong Zhang, Shihong Lao, and Xilin Chen. Cross-view graph embedding. In Asian Conference on Computer Vision, pages 770-781. Springer, 2012.","title":"Cross-view graph embedding","authors":"Zhiwu Huang, Shiguang Shan, Haihong Zhang, Shihong Lao, Xilin Chen","year":"2012","venue":"Asian Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2018] Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V Chawla. Deepcrime: attentive hierarchi- cal recurrent networks for crime prediction. In Proceed- ings of the 27th ACM International Conference on Infor- mation and Knowledge Management, pages 1423-1432. ACM, 2018.","title":"Deepcrime: attentive hierarchical recurrent networks for crime prediction","authors":"Chao Huang, Junbo Zhang, Yu Zheng, Nitesh V Chawla","year":"2018","venue":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.","title":"Variational graph auto-encoders","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Pan et al., 2012] Gang Pan, Guande Qi, Zhaohui Wu, Daqing Zhang, and Shijian Li. Land-use classification using taxi gps traces. IEEE Transactions on Intelligent Transportation Systems, 14(1):113-123, 2012.","title":"Land-use classification using taxi gps traces","authors":"Pan","year":"2012","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of the 20th ACM SIGKDD in- ternational conference on Knowledge discovery and data mining, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Qu et al., 2017] Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, and Jiawei Han. An attention-based col- laboration framework for multi-view network representa- tion learning. In Proceedings of the 2017 ACM on Confer- ence on Information and Knowledge Management, pages 1767-1776. ACM, 2017.","title":"An attention-based collaboration framework for multi-view network representation learning","authors":"Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han","year":"2017","venue":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Roweis and Saul, 2000] Sam T Roweis and Lawrence K Saul. Nonlinear dimensionality reduction by locally lin- ear embedding. science, 290(5500):2323-2326, 2000.","title":"Nonlinear dimensionality reduction by locally linear embedding","authors":"T Sam, Lawrence K Roweis, Saul","year":"2000","venue":"science","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Su et al., 2015] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view con- volutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on com- puter vision, pages 945-953, 2015.","title":"Multi-view convolutional neural networks for 3d shape recognition","authors":"Su","year":"2015","venue":"Proceedings of the IEEE international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Sun et al., 2018] Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, and Vasant Honavar. Multi-view network embedding via graph factorization clustering and co-regularized multi- view agreement. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pages 1006-1013. IEEE, 2018.","title":"Multi-view network embedding via graph factorization clustering and co-regularized multiview agreement","authors":"Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant Honavar","year":"2018","venue":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of the 24th international conference on world wide web, pages 1067-1077. International World Wide Web Confer- ences Steering Committee, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of the 24th international conference on world wide web","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Tibshirani, 1996] Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267-288, 1996.","title":"Regression shrinkage and selection via the lasso","authors":"Robert Tibshirani","year":"1996","venue":"Journal of the Royal Statistical Society: Series B (Methodological)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing sys- tems, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Veličković et al., 2017] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.","title":"Graph attention networks","authors":"Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang and Li, 2017] Hongjian Wang and Zhenhui Li. Re- gion representation learning via mobility flow. In Proceed- ings of the 2017 ACM on Conference on Information and Knowledge Management, pages 237-246. ACM, 2017.","title":"Region representation learning via mobility flow","authors":"Wang, Li ; ] Hongjian Wang, Zhenhui Li","year":"2017","venue":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yao et al., 2018] Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, and Hui Xiong. Representing urban functions through zone embedding with human mobility patterns. In IJCAI, pages 3919-3925, 2018.","title":"Representing urban functions through zone embedding with human mobility patterns","authors":"Zijun Yao, Yanjie Fu, Bin Liu, Wangsu Hu, Hui Xiong","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/545"},{"position":21,"raw":"[Zhang et al., 2017] Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, and Jiawei Han. Regions, periods, activities: Un- covering urban dynamics via cross-modal representation learning. In Proceedings of the 26th International Con- ference on World Wide Web, pages 361-370. International World Wide Web Conferences Steering Committee, 2017.","title":"Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning","authors":"Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, Jiawei Han","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2019] Yunchao Zhang, Yanjie Fu, Pengyang Wang, Xiaolin Li, and Yu Zheng. Unifying inter-region autocorrelation and intra-region structures for spatial em- bedding via collective adversarial learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1700-1708. ACM, 2019.","title":"Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning","authors":"Zhang","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zheng et al., 2014] Yu Zheng, Tong Liu, Yilun Wang, Yan- min Zhu, Yanchi Liu, and Eric Chang. Diagnosing new york city's noises with ubiquitous data. In Proceedings of the 2014 ACM International Joint Conference on Per- vasive and Ubiquitous Computing, pages 715-725. ACM, 2014.","title":"Diagnosing new york city's noises with ubiquitous data","authors":"Yu Zheng, Tong Liu, Yilun Wang, Yanmin Zhu, Yanchi Liu, Eric Chang","year":"2014","venue":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}