{"type":"ScholarlyArticle","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","authors":[{"name":"Zonghan Wu","slug":"zonghan-wu","given":"Zonghan","family":"Wu","affiliation":"Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"Faculty of Information Technology , Monash University , Australia"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"},{"name":"Jing Jiang","slug":"jing-jiang","given":"Jing","family":"Jiang","affiliation":"Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":"Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"}],"abstract":"Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.  Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However,  the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture,  {Graph WaveNet}, for spatial-temporal  graph modeling. By developing a novel adaptive dependency matrix and learn it  through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.","doi":"10.24963/ijcai.2019/264","url":"https://ijcai.org/proceedings/2019/264","pdf":"https://ijcai.org/proceedings/2019/0264.pdf","markdown":"https://ijcai.org/proceedings/2019/264.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/264","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":264,"pages":{"first":1907,"last":1913},"keywords":["Machine Learning: Relational Learning","Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning","Machine Learning Applications: Networks","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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