Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...