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- Yiding Yang
- Department of Computer Science, Stevens Institute of Technology
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- Xinchao Wang
- Department of Computer Science, Stevens Institute of Technology
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- Mingli Song
- College of Computer Science and Technology, Zhejiang University
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- Junsong Yuan
- Department of Computer Science and Engineering, State University of New York at Buffalo
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- Dacheng Tao
- UBTECH Sydney Artificial Intelligence Centre, University of Sydney
書誌事項
- 公開日
- 2019-08
- DOI
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- 10.24963/ijcai.2019/569
- 公開者
- International Joint Conferences on Artificial Intelligence Organization
説明
<jats:p>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.</jats:p>
収録刊行物
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- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence 4099-4105, 2019-08
International Joint Conferences on Artificial Intelligence Organization