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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

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arxiv 1803.07294 v1 pith:7DRD4IHS submitted 2018-03-20 cs.LG cs.SI

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , Dit-Yan Yeung This is my paper
classification cs.LG cs.SI
keywords gaanattentiongatedgraphslearningnetworksproblemachieves
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks.

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Forward citations

Cited by 6 Pith papers

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    GraphStar is a new GNN that adds star nodes and relay attention to achieve non-local representations for node, graph, and link tasks, claiming 2-5% gains over prior SOTA on benchmarks.

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  5. A Global-Local Graph Attention Network for Traffic Forecasting

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