PSA-GNN: An augmented GNN framework with priori subgraph knowledge

Abstract

Graph neural networks have become the primary graph representation learning paradigm, in which nodes update their embeddings by aggregating messages from their neighbors iteratively. However, current message passing based GNNs exploit the higher-order subgraph information other than 1st-order neighbors insufficiently. In contrast, the long-standing graph research has investigated various subgraphs such as motif, clique, core, and truss that contain important structural information to downstream tasks like node classification, which deserve to be preserved by GNNs. In this work, we propose to use the pre-mined subgraphs as priori knowledge to extend the receptive field of GNNs and enhance their expressive power to go beyond the 1st-order Weisfeiler-Lehman isomorphism test. For that, we introduce a general framework called PSA-GNN (Priori Subgraph Augmented Graph Neural Network), which augments each GNN layer by a pair of parallel convolution layers based on a bipartite graph between nodes and priori subgraphs. PSA-GNN intrinsically builds a hybrid receptive field by incorporating priori subgraphs as neighbors, while the embeddings and weights of subgraphs are trainable. Moreover, PSA-GNN can purify the noisy subgraphs both heuristically before training and deterministically during training based on a novel metric called homogeneity. Experimental results show that PSA-GNN achieves an improved performance compared with state-of-the-art message passing based GNN models.