Graph Distribution Shift

Graph distribution shift, a critical challenge in graph neural network (GNN) applications, arises when the structure or features of training and test graphs differ significantly, hindering model generalization. Current research focuses on developing methods to mitigate this shift, including techniques like pairwise alignment of node representations, mixture-of-experts models that handle diverse shift types, and structural re-weighting to address conditional structure shifts. Addressing graph distribution shift is crucial for reliable deployment of GNNs in real-world scenarios, improving the accuracy and robustness of applications ranging from social network analysis to high-energy physics.