Robust Graph Structure Learning under Heterophily

Understand

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 different classes.