Path-enhanced graph convolutional networks for node classification without features.

ABSTRACT

Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the performance of graph convolutional networks (GCNs) on the graphs without node features. In order to resolve the issue, we propose a method called t-hopGCN to describe t-hop neighbors by the shortest path between two nodes, then the adjacency matrix of t-hop neighbors as features to perform node classification. Experimental results show that t-hopGCN can significantly improve the performance of node classification in the graphs without node features. More importantly, adding the adjacency matrix of t-hop neighbors can improve the performance of existing popular GNNs on node classification.