부산대학교 도서관

In recent years, heterogeneous graph neural networks have become the mainstream approach for handling heterogeneous graph data. However, due to the sparsity of labels, most existing methods for heterogeneous graph neural networks typically employ a semi-supervised learning approach, which has certain limitations in practical applications. To address this issue, we propose a self-supervised heterogeneous graph representation learning method, namely Multi-scale Heterogeneous Graph Contrastive Learning (MHGCL). This approach decodes encoded information from two perspectives: meta-paths and network patterns, in a multi-scale fashion. It uses a loss function that maximizes the similarity between positive pairs at different scales and minimizes the similarity between negative pairs. This encourages related nodes and edges to be close to each other in the embedding space, while unrelated nodes and edges are pushed farther apart. Experimental results demonstrate that MHGCL comprehensively captures semantic information at different scales between nodes. It exhibits excellent performance in node classification tasks, validating its effectiveness in heterogeneous graph node embedding learning.