Contrastive Learning for Signed Bipartite Graphs

This paper is the first to use contrastive learning to improve the robustness of graph representation learning for signed bipartite graphs, which are commonly found in social networks, recommender systems, and paper review platforms. Existing contrastive learning methods for signed graphs cannot capture implicit relations between nodes of the same type in signed bipartite graphs, which have two types of nodes and edges only connect nodes of different types. We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. SBGCL augments a signed bipartite graph with a novel two-level graph augmentation method. At the top level, we maintain two perspectives of the signed bipartite graph, one presents the original interactions between nodes of different types, and the other presents the implicit relations between nodes of the same type. At the bottom level, we employ stochastic perturbation strategies to create two perturbed graphs in each perspective. Then, we construct positive and negative samples from the perturbed graphs and design a multi-perspective contrastive loss to unify the node presentations learned from the two perspectives. Results show proposed model is effective over state-of-the-art methods on real-world datasets.