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Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which has aroused increasing research interest in recent years. Simultaneously, signed graphs consisting of both positive and negative links have become ubiquitous with the growing popularity of social media. However, existing works on graph contrastive representation learning are only proposed for unsigned graphs (containing only positive links) and it remains unexplored how they could be applied to signed graphs due to the distinct semantics and complex relations between positive and negative links. Therefore we propose a novel Signed Graph Contrastive Learning model (SGCL) to bridge this gap, which to the best of our knowledge is the first research to employ graph contrastive representation learning on signed graphs. Concretely, we design two types of graph augmentations specific to signed graphs based on a significant signed social theory, i.e., balance theory. Besides, inter-view and intra-view contrastive learning are proposed to learn discriminative node representations from perspectives of graph augmentations and signed structures respectively. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods on both real-world social datasets and online game datasets. |
