Dual Graph Complementary Network

Chenhua Liu, Kun Zhan

Abstract: As a powerful representation learning method on graph data, graph neural networks (GNNs) have shown great popularity in tackling graph analytic problems. Although many attempts have been made in literatures to find strategies about extracting better embedding of the target nodes, few of them consider this issue from a comprehensive perspective. Most of current GNNs usually employ some single method which can commendably extract a certain kind of feature but some equally important features are often ignored. In this paper, we develop a novel dual graph complementary network (DGCN) to learn representation complementarily. We use two different branches, and inputs of the two branches are the same, which are composed of structure and feature information. At the same time, there is also a complementary relationship between the two branches. Beyond that, our extensive experiments show that DGCN outperforms state-of-the-art methods on five public benchmark datasets.

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