Graph representation learning via redundancy reduction

Abstract

In recent years, self-supervised learning methods based on mutual information maximization have achieved remarkable success on graph data tasks. However, most of them heavily rely on a large number of negative samples, which is computationally expensive. These methods also fail to extract the semantic cluster information of the data. To overcome these problems, we propose a novel self-supervised approach called Graph Representation Learing via Redundancy Reduction (GRRR) to learn node representations based on the redundancy-reduction principle. The proposed GRRR preserves as much topological information of the graph as possible, and minimizes the redundancy of representation in terms of node instance and semantic cluster information. Specifically, we first design three graph data augmentation strategies to construct two augmented views. Then, to filter the redundant information in each augmented view, we propose the self-redundancy reduction module which implements the structural reconstruction. Finally, we propose the joint redundancy reduction module to further filter undesirable information via a cross-view approach. It preserves the most essential instance features and semantic cluster information by maximizing the agreement across different views not only on node instance features but also on cluster assignments. Results on several benchmark datasets show that GRRR outperforms state-of-the-art methods in downstream node classification, link prediction, and node clustering tasks.