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arXiv · 1910.01736

Context-Aware Graph Attention Networks

Bo Jiang·Leiling Wang·Jin Tang·Bin Luo
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Abstract

Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation. In this paper, we propose a novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT). CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges. In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training. We apply CaGAT on semi-supervised learning tasks. Promising experimental results on several benchmark datasets demonstrate the effectiveness and benefits of CaGAT.

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BibTeXRIS

Bo Jiang, Leiling Wang, Jin Tang, Bin Luo. 2019-09-04. Context-Aware Graph Attention Networks. https://arxiv.org/abs/1910.01736

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