DHGNN-MVC: Dynamic heterogeneous graph neural network based on multi-view contrastive learning.

Abstract

Dynamic heterogeneous graphs, as an important tool for characterizing multiple types of entities and their evolutionary relationships in complex systems, have been widely used in scenarios such as academic network analysis, e-commerce, and social networks. However, compared to static homogeneous graphs, the heterogeneity and temporal dynamics in dynamic heterogeneous graph data pose additional challenges for representation learning, meaning that existing dynamic heterogeneous graph learning methods still encounter the following challenges during modeling: (1) There is a problem of limited semantic propagation of meta-paths within time snapshots; (2) There is a low-order relationship bias problem caused by dynamic optimization objectives. To address the above challenges, we propose a dynamic heterogeneous graph neural network based on multi-view contrastive learning (DHGNN-MVC). Specifically, we explicitly introduce the attribute information of intermediate nodes in the meta-path during the heterogeneous information aggregation process by optimizing the meta-path semantic modeling approach. Combined with a message propagation mechanism based on the PPR matrix, this allows us to collaboratively capture both local and deep information in heterogeneous graph snapshots from the perspectives of relationships and meta-paths, thereby effectively alleviating the limitations of meta-path semantic transmission within temporal snapshots. At the same time, we propose a multi-view contrastive learning strategy that jointly optimizes the future graph reconstruction loss, local-global contrastive loss, and community evolution contrastive loss. The node representations are constrained at three scales: local structure, graph level semantics, and community level to effectively alleviate the problem of low-order relationship bias. The simulation experiments conducted on multiple real-world dynamic heterogeneous graph datasets show that the performance of DHGNN-MVC is significantly better than existing baseline methods, fully demonstrating its effectiveness and novelty in capturing the evolution laws of dynamic heterogeneous graph networks.