AI RESEARCH
Graph Topology Information Enhanced Heterogeneous Graph Representation Learning
arXiv CS.LG
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ArXi:2604.05732v1 Announce Type: new 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.