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arXiv · 2010.04554

Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution

Yucheng Lin·Huiting Hong·Xiaoqing Yang·Xiaodi Yang·Pinghua Gong·Jieping Ye
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Abstract

Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal node/edge features. However, most existing methods only take part of the information into consideration. In this paper, we present the Co-evolved Meta Graph Neural Network (CoMGNN), which applies meta graph attention to heterogeneous graphs with co-evolution of node and edge states. We further propose a spatiotemporal adaption of CoMGNN (ST-CoMGNN) for modeling spatiotemporal patterns on nodes and edges. We conduct experiments on two large-scale real-world datasets. Experimental results show that our models significantly outperform the state-of-the-art methods, demonstrating the effectiveness of encoding diverse information from different aspects.

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Yucheng Lin, Huiting Hong, Xiaoqing Yang, Xiaodi Yang, Pinghua Gong, Jieping Ye. 2020-10-09. Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution. https://arxiv.org/abs/2010.04554

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