HGRL-S: Towards Heterogeneous Graph Representation Learning With Optimized Structures

Abstract

Heterogeneous Graph Neural Networks (HetGNN) have garnered significant attention and demonstrated success in tackling various tasks. However, most existing HetGNNs face challenges in effectively addressing unreliable heterogeneous graph structures and encounter semantic indistinguishability problems as their depth increases. In an effort to deal with these challenges, we introduce a novel heterogeneous graph representation learning with optimized structures to optimize heterogeneous graph structures and utilize semantic aggregation mechanism to alleviate semantic indistinguishability while learning node embeddings. To address the heterogeneity of relations within heterogeneous graphs, the proposed algorithm employs a strategy of generating distinct relational subgraphs and incorporating them with node features to optimize structural learning. To resolve the issue of semantic indistinguishability, the proposed algorithm adopts a semantic aggregation mechanism to assign appropriate weights to different meta-paths, consequently enhancing the effectiveness of captured node features. This methodology enables the learning of distinguishable node embeddings by a deeper HetGNN model. Extensive experiments on the node classification task validate the promising performance of the proposed framework when compared with state-of-the-art methods.