Graphs Generalization under Distribution Shifts

arXiv:2403.16334v1 Announce Type: new
Abstract: Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution. To address this crucial issue, out-of-distribution (OOD) generalization, which aims to achieve satisfactory generalization performance when faced with unknown distribution shifts, has made a significant process. However, the OOD method for graph-structured data currently lacks clarity and remains relatively unexplored due to two primary challenges. Firstly, distribution shifts on graphs …

abstract arxiv cs.ai cs.lg distribution graphs independent issue limitations machine machine learning performance test traditional machine learning training type