Robust Heterophily Graph Learning via Uniformity Augmentation

Graphs serve as fundamental representations for a diverse array of complex systems, capturing intricate relationships and interactions between entities. In many real-world scenarios, graphs exhibit non-homophilous, or heterophilous, characteristics, challenging traditional graph analysis methods rooted in homophily assumptions. Recent heterophilous methods frequently struggle with noise in node attributes, which can degrade the quality of graph representations and affect downstream task performance. Common graph augmentations, while useful, often introduce bias and irrelevant noise. This paper proposes a novel method, Robust Heterophily Graph Learning via Uniformity Augmentation (RHGL-UA), which incorporates uniformity in the augmentation process through controlled random perturbations. This approach ensures a more uniform distribution of representations across different layers of the model. By adapting to data variations and learning more diverse information, RHGL-UA significantly improves performance on downstream tasks and stands out as the first practical robust heterophily graph method using representation augmentation with a theoretical guarantee. Extensive experiments demonstrate the merit of our proposed method.