Improving the Homophily of Heterophilic Graphs for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have been applied to process the widespread graph data, including social networks and web data, etc. However, lots of GNNs can only perform well on homophilic graphs, while losing their superiority when tackling heterophilic graphs. Recent works try to use spectral theory or attention mechanism to design some more complex learning paradigms for heterophilic graphs. In this paper, we instead utilize some explored properties to construct three new graph structures of high homophily to improve the homophily of heterophilic graphs for better representation learning. Along with the original graph structure, totally four graph structures are injected into a Multi-View Graph Fusion Network (MVGFN) to learn a group of more expressive features for the semi-supervised node classification. Ablation experiments show that all three newly-constructed graph structures obtain higher homophily levels. Comparisons among several baselines indicate the superiority of our method on both homophilic and heterophilic graphs.