Information Fusion of Topological Structure and Node Features in Graph Neural Network

Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, which plays an import role in GNNs. Besides, datasets in the real world often have noises, which make the information fusion difficult. In this paper, we give an information-theoretic explanation. Specifically, we focus on how the information from topological structures and node features fuses and how different information contributes to the downstream task. Furthermore, we propose a general framework named M-GCN to express the fusion process in GNNs. Graph embeddings and feature graph are introduced to extract the information from topological structure and node features separately in M-GCN. Extensive experiments are conducted on several benchmark datasets and experimental results show that our proposed models are more robust and outperform state-of-the-art methods.