🤖 AI Summary
This work addresses the challenge of representation learning in heterogeneous graphs where heterophily and structural noise coexist. To tackle this issue, the authors propose HGUL, a unified framework that, for the first time, integrates heterophily modeling and structural denoising within a single end-to-end architecture. HGUL synergistically combines three components: kNN-based graph reconstruction, adaptive graph structure refinement, and heterogeneous affinity learning via polynomial graph kernels, effectively capturing cross-class node relationships while suppressing noise interference. Extensive experiments demonstrate that HGUL significantly outperforms existing methods across multiple benchmark datasets, exhibiting exceptional robustness in noisy scenarios. These results substantiate the efficacy and necessity of jointly modeling heterophily and structural noise for learning robust representations in heterogeneous graphs.
📝 Abstract
Heterogeneous graphs with heterophily have emerged as a powerful abstraction for modeling complex real-world systems, where nodes of different types and labels interact in diverse and often non-homophilous ways. Despite recent advances, robust representation learning for such graphs remains largely unexplored, particularly in the presence of noisy or misleading connectivity. In this work, we investigate this problem and identify structural noise as a critical challenge that significantly degrades model performance. To address this issue, we propose a unified framework, Heterogeneous Graph Unified Learning (HGUL), which jointly handles heterophily and noisy graph structures. The framework consists of three complementary modules: a kNN-based graph construction module that recovers reliable local neighborhoods, a graph structure learning module that adaptively refines the adjacency by filtering noisy edges, and a heterogeneous affinity learning module that captures class-level relationships via an extended affinity matrix derived from a polynomial graph kernel. Extensive experiments on multiple datasets demonstrate that HGUL consistently outperforms existing methods on clean graphs and maintains strong robustness under varying levels of structural noise. The results further underscore the importance of jointly modeling heterophily and noise in heterogeneous graph learning.