Robust Graph Structure Learning under Heterophily

arXiv:2403.03659v1 Announce Type: new
Abstract: Graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assume a given graph to be accurate and complete. However, real data is inevitably noisy and sparse, which will lead to inferior results. Despite the remarkable success of recent graph representation learning methods, they inherently presume that the graph is homophilic, and largely overlook heterophily, where most connected nodes are from …

abstract arxiv cs.lg data graph however objects real data relations results robust tasks type will