Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective

Understand

Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data.

• However, in real-world scenarios, this assumption may not always be valid.
• Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs.
• Most existing efforts have primarily concentrated on improving graph OOD generalization from two \textbf{model-agnostic} perspectives: data-driven methods and strategy-based learning.