[2604.05732] Graph Topology Information Enhanced Heterogeneous Graph Representation Learning

Computer Science > Machine Learning arXiv:2604.05732 (cs) [Submitted on 7 Apr 2026] Title:Graph Topology Information Enhanced Heterogeneous Graph Representation Learning Authors:He Zhao, Zhiwei Zeng, Yongwei Wang, Chunyan Miao View a PDF of the paper titled Graph Topology Information Enhanced Heterogeneous Graph Representation Learning, by He Zhao and 3 other authors View PDF HTML (experimental) Abstract:Real-world heterogeneous graphs are inherently noisy and usually not in the optimal graph structures for downstream tasks, which often adversely affects the performance of GRL models in downstream tasks. Although Graph Structure Learning (GSL) methods have been proposed to learn graph structures and downstream tasks simultaneously, existing methods are predominantly designed for homogeneous graphs, while GSL for heterogeneous graphs remains largely unexplored. Two challenges arise in this context. Firstly, the quality of the input graph structure has a more profound impact on GNN-based heterogeneous GRL models compared to their homogeneous counterparts. Secondly, most existing homogenous GRL models encounter memory consumption issues when applied directly to heterogeneous graphs. In this paper, we propose a novel Graph Topology learning Enhanced Heterogeneous Graph Representation Learning framework (ToGRL).ToGRL learns high-quality graph structures and representations for downstream tasks by incorporating task-relevant latent topology information. Specifically, a novel GSL...