Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

• URL: http://arxiv.org/abs/2306.07608v1
• Date: Tue, 13 Jun 2023 08:06:10 GMT
• Title: Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
• Authors: Yizhen Zheng, He Zhang, Vincent CS Lee, Yu Zheng, Xiao Wang, Shirui Pan
• Abstract summary: Graphs with homophily-prone edges tend to connect nodes with the same class. Heterophily-prone edges tend to build relationships between nodes with different classes. Existing GNNs only take the original graph during training.
• Score: 48.79929516665371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build relationships between nodes with different classes (i.e., inter-class nodes). Existing GNNs only take the original graph during training. The problem with this approach is that it forgets to take into consideration the ``missing-half" structural information, that is, heterophily-prone topology for homophily-prone graphs and homophily-prone topology for heterophily-prone graphs. In our paper, we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two components: graph complementation and complemented graph convolution. The first component finds the missing-half structural information for a given graph to complement it. The complemented graph has two sets of graphs including both homophily- and heterophily-prone topology. In the latter component, to handle complemented graphs, we design a new graph convolution from the perspective of optimisation. The experiment results show that GOAL consistently outperforms all baselines in eight real-world datasets.

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