NEAR: Neighborhood Edge AggregatoR for Graph Classification

Cheolhyeong Kim 1, Haeseong Moon 2, and Hyung Ju Hwang † 1 1 Department of Mathematics, POSTECH 2 Department of Mathematics, University of California, San Diego Abstract Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis and data mining. Recent GNN algorithms are based on neural message passing, which enables GNNs to integrate local structures and node features recursively. However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships. In this paper, we propose Neighborhood Edge AggregatoR (NEAR), a novel framework that aggregates relations between the nodes in the neighborhood via edges. NEAR, which can be orthogonally combined with previous GNN algorithms, gives integrated information that describes which nodes in the neighborhood are connected. Therefore, GNNs combined with NEAR reflect each node's local structure beyond the nodes themselves. Experimental results on multiple graph classification tasks show that our algorithm achieves state-of-the-art results. 1 Introduction Interest in learning graph structured data has risen rapidly in recent years because of its wide applicability in bioinformatics, chemoinformatics, social network analysis and data mining. For learning graph-structured data, we need an algorithm that can effectively represent the graph structure and relations between the graph nodes.