SGCA: Signed Graph Contrastive Learning with Adaptive Augmentation

Graph contrastive learning (GCL) enables graph neural networks (GNNs) to learn generalized node features and achieve better performance on downstream tasks. Meanwhile, with the rapid growth of social media, signed graphs have attracted people's attention, for they can represent complex relationships among people with positive and negative links. Many efforts for unsigned GCL have been made, while signed GCL is rarely explored. This paper argues that unsigned GCL cannot be directly transferred to signed graphs for two reasons. Firstly, unsigned GCL performs random augmentation on the input graph to generate multi-views for contrast, destroying signed graphs' balance property. Secondly, when the balance is broken, relationships between nodes become ambiguous, leading to over-smoothing between different channels. Therefore, we propose a novel signed GCL model called Signed Graph Contrastive Learning with Adaptive Augmentation (SGCA) to solve these problems. Specifically, we use a learnable module to generate balanced augmented views adaptively. At the same time, we add a constraint to the signed graph encoder to alleviate the over-smoothing problem caused by the balance violation. Various experiments demonstrate the superiority of SGCA over 12 competitive methods on 5 real-world datasets.