Graph Learning under Distribution Shifts: A Comprehensive Survey

In this comprehensive survey, the authors explore the challenges of distribution shifts in graph learning and present various methods to address them, categorizing existing approaches into three essential scenarios. They aim to provide guidance for developing effective graph learning algorithms and stimulate future research in this area.