IS-GNN: Graph neural network enhanced by aggregating influential and structurally similar nodes

Abstract

Nowadays, it has been demonstrated that graph neural networks (GNNs) are a powerful tool for graph representation learning. However, most GNN models are shallow ones because stacking more layers may cause the over-smoothing problem. In addition, conventional neighborhood aggregation GNNs have an implicit homophily assumption, leading to poor performance when dealing with networks with heterophily. To address the aforementioned issues, we present a new GNN model, called IS-GNN, to effectively enhance the representation quality of nodes in general networks. IS-GNN learns the representation of each node using three aspects of information: local structural information from its neighborhood, global topological information from influential nodes, and homophilous information from its structurally similar nodes. Consequently, the representations of nodes are more powerful and discriminative. The effectiveness of IS-GNN is validated on both homophilous and heterophilous networks. The experimental results of semi-supervised node classification manifest that the accuracy of IS-GNN is superior to that of baselines.