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

• URL: http://arxiv.org/abs/2010.04554v1
• Date: Fri, 9 Oct 2020 13:19:39 GMT
• Title: Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
• Authors: Yucheng Lin, Huiting Hong, Xiaoqing Yang, Xiaodi Yang, Pinghua Gong, Jieping Ye
• Abstract summary: We present Coevolved Meta Graph Neural Network (CoMGNN), which applies meta graph attention to heterogeneous graphs with co-evolution of edges and node states. We also propose CoMGNN (ST-CoMGNN) for modelingtemporal patterns on nodes and edges.
• Score: 44.90253939019069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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