{"type":"ScholarlyArticle","title":"SPAGAN: Shortest Path Graph Attention Network","authors":[{"name":"Yiding Yang","slug":"yiding-yang","given":"Yiding","family":"Yang","affiliation":"Department of Computer Science , Stevens Institute of Technology"},{"name":"Xinchao Wang","slug":"xinchao-wang","given":"Xinchao","family":"Wang","affiliation":"Department of Computer Science , Stevens Institute of Technology"},{"name":"Mingli Song","slug":"mingli-song","given":"Mingli","family":"Song","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Junsong Yuan","slug":"junsong-yuan","given":"Junsong","family":"Yuan","affiliation":"Department of Computer Science and Engineering , State University of New York at Buffalo"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"UBTECH Sydney Artifical Intelligence Centre , University of Sydney"}],"abstract":"Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions, on either first-order neighbors or random higher-order ones, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further the more effective aggregation of information from distant neighbors, as compared to node-based GCN methods. We test SPAGAN for the downstream classification task on several standard datasets, and achieve performances superior to the state of the art.","doi":"10.24963/ijcai.2019/569","url":"https://ijcai.org/proceedings/2019/569","pdf":"https://ijcai.org/proceedings/2019/0569.pdf","markdown":"https://ijcai.org/proceedings/2019/569.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/569","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":"Machine Learning M-Z","track":"Main track","number":569,"pages":{"first":4099,"last":4105},"keywords":["Computer Vision: Structural and Model-Based Approaches, Knowledge Representation and Reasoning","Machine Learning: Dimensionality Reduction and Manifold Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Deep Learning","Knowledge Representation and Reasoning: Knowledge Representation and Decision ; Utility Theory","Machine Learning: Learning Graphical Models","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Abu-El-Haija et al., 2018] Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, and Joonseok Lee. 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