SBP-GCA: social behavior prediction via graph contrastive learning with attention

Social behavior prediction on social media is attracting significant attention from researchers. Social e-commerce focuses on engagement marketing, which emphasizes social behavior because it effectively increases brand recognition. Currently, existing works on social behavior prediction suffer from two main problems: 1) they assume that social influence probabilities can be learned independently of each other, and their calculations do not include any influence probability estimations based on friends' behavior; and 2) negative sampling of subgraphs is usually ignored in social behavior prediction work. To the best of our knowledge, introducing graph contrastive learning (GCL) to social behavior prediction is novel and interesting. In this article, we propose a framework, social behavior prediction via graph contrastive learning with attention named SBP-GCA, to promote social behavior prediction. First, two methods are designed to extract subgraphs from the original graph, and their structural features are learned by GCL. Then, it models how a user's behavior is influenced by neighbors and learns influence features via graph attention networks (GATs). Furthermore, it combines structural features, influence features, and intrinsic features to predict social behavior. Extensive and systematic experiments on three datasets validate the superiority of the proposed SBP-GCA.