MC-GNN: Multi-Channel Graph Neural Networks With Hilbert-Schmidt Independence Criterion

Abstract

Graph Neural Networks (GNNs) have been proven to be useful for learning graph-based knowledge. However, one of the drawbacks of GNN techniques is that they may get stuck in the problem of over-squashing. Recent studies attribute to the message passing paradigm that it may amplify some specific local relations and distort long-range information under a certain GNN. To alleviate such phenomena, we propose a novel and general GNN framework, dubbed MC-GNN, which introduces the multi-channel neural architecture to learn and fuse multi-view graph-based information. The purpose of MC-GNN is to extract distinct channel-based graph features and adaptively adjust the importance of the features. To this end, we use the Hilbert-Schmidt Independence Criterion (HSIC) to enlarge the disparity between the embeddings encoded by each channel and follow an attention mechanism to fuse the embeddings with adaptive weight adjustment. MC-GNN can apply multiple GNN backbones, which provides a solution for learning structural relations from a multi-view perspective. Experimental results demonstrate that the proposed MC-GNN is superior to the compared state-of-the-art GNN methods.