M2GNN: Multi-scale Multi-channel Graph Neural Network

Abstract

Aiming at the problem that existing graph neural network architectures usually use a single scale to process graph data, which leads to information loss and simplification, this paper proposes a novel graph neural network approach, the M2GNN framework, which aims to enhance the feature learning capability of graph structured data through multi-scale fusion and attention mechanism. In M2GNN, each channel handles graph features at different scales separately, and integrates local and global information using multi-scale fusion methods to capture features at different levels in the graph structure. The learned features from each channel are then weighted and fused using an attention mechanism to extract the most representative feature representation. The experimental results show that compared with the traditional graph neural network approach, M2GNN improves the performance by 0.70% to 54.14%, 0.34% to 54.31%, and 0.68% to 54.40% for the node classification task with different label coverages, which verifies the effectiveness of the multi-channel and multi-scale fusion strategies.