A Flexible Diffusion Convolution for Graph Neural Networks

Abstract

Graph Neural Networks (GNNs) have been gaining more attention due to their excellent performance in modeling various graph-structured data. However, most of the current GNNs only consider fixed-neighbor discrete message-passing, disregarding the importance of the local structure of different nodes and the implicit information between nodes for smoothing features. Previous approaches either focus on adaptive selection for aggregation structures or treat discrete graph convolution as a continuous diffusion process, but none of them comprehensively considered the above issues, significantly limiting the model's performance. To this end, we present a novel approach called Flexible Diffusion Convolution (Flexi-DC), which exploits the neighborhood information of nodes to set a particular continuous diffusion for each node to smooth features. Specifically, Flexi-DC first extracts the local structure knowledge based on the degrees of nodes in the graph data and then injects it into the diffusion convolution module to smooth features. Additionally, we utilize the extracted knowledge to smooth labels. Flexi-DC is an efficient framework that can significantly improve the performance of most GNN architectures. Experimental results demonstrate that Flexi-DC outperforms their vanilla implementations by an average accuracy of 13.24% (GCN), 16.37% (JKNet), and 11.98% (ARMA) on nine graph datasets with different homophily ratios.