Graph Information Interaction on Feature and Structure via Cross-modal Contrastive Learning

The abundant features and structure information on graphs provide a potential guarantee for learning high-quality representations without supervision. Feature attribute represents the inherent properties of nodes, while structure attribute describes their neighborhood relationship. These two types of attributes can be regarded as different modal forms of the same instance and should be consistent in identifying a member. We propose to directly regard feature and structure attributes as two separate views to embed this consistency into contrastive learning method, realizing graph information interaction on feature and structure in a cross-modal contrastive framework. Under this framework, node representations are learned in an unsupervised manner by maximizing the agreement between feature representation and structure representation. In terms of negative samples, instead of randomly sampling points from empirical distribution, a simple yet effective multi-sample mixing strategy is proposed to synthesize true negative samples with greater probability, alleviating the tricky false negative issue. Extensive experiments on multiple types of graphs demonstrate the effectiveness of the proposed method.