Proximity Enhanced Graph Neural Networks with Channel Contrast

Abstract

We consider graph representation learning in an unsupervised manner. Graph neural networks use neighborhood aggregation as a core component that results in feature smoothing among nodes in proximity. While successful in various prediction tasks, such a paradigm falls short of capturing nodes' similarities over a long distance, which proves to be important for high-quality learning. To tackle this problem, we strengthen the graph with three types of additional graph views, in which each node is directly linked to a set of nodes with the highest similarity in terms of node features, neighborhood features or local structures. Not restricted by connectivity in the original graph, the generated views provide new and complementary perspectives from which to look at the relationship between nodes. Inspired by the recent success of contrastive learning approaches, we propose a self-supervised method that aims to learn node representations by maximizing the agreement between representations across generated views and the original graph, without the requirement of any label information. We also propose a channel-level contrast approach that greatly reduces computation cost. Extensive experiments on six assortative graphs and three disassortative graphs demonstrate the effectiveness of our approach.