A Language-Assisted Semantic-Aware Disentangled Method for Link Prediction on Heterogeneous Graphs

Link prediction serves as a fundamental task in graph-based applications, where graph neural networks (GNNs) are extensively applied to estimate node connectivity likelihood. However, GNN-based methods for homogeneous graphs encounter the semantic mixing issue in heterogeneous graphs. Previous works leverage the disentangled-based model to separate the semantic information into different factors and conduct the message passing for link prediction. However, their models suffer from information loss and inadequate expression, which harms link prediction performance. To address these limitations, we propose a language-assisted semantic-aware disentangled method for link prediction on heterogeneous graphs. First, we employ a factor-wise attention mechanism to reduce the information loss caused by the disentangled model. Specifically, we design a factor selection strategy to select disentangled factors and combine them to utilize more semantic information. Second, a language-graph learning method is developed to enhance contextual expression by fusing the features of nodes and edge textual information. Extensive experiments show that the proposed method outperforms existing state-of-the-art baselines.