SOTAVerified

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

2018-03-20Code Available0· sign in to hype

Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, Dit-yan Yeung

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

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.

Tasks

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
PPIGaANF198.7Unverified

Reproductions