  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 framework that
aggregates relations between the nodes in the neighborhood via edges. NEAR,
which can be orthogonally combined with Graph Isomorphism Network (GIN), gives
integrated information that describes which nodes in the neighborhood are
connected. Therefore, NEAR can reflect additional information of a local
structure of each node beyond the nodes themselves in 1-hop neighborhood.
Experimental results on multiple graph classification tasks show that our
algorithm makes a good improvement over other existing 1-hop based GNN-based
algorithms.
