GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts

Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning architectures that generalize to complex non-synthetic distributional shifts naturally occurring in the real world. Here we develop GraphMETRO, a Graph Neural Network architecture, that reliably models natural diversity and captures complex distributional shifts. GraphMETRO employs a Mixture-of-Experts (MoE) architecture with a gating model and multiple expert models, where each expert model targets a specific distributional …

architecture architectures build cs.lg data distribution diversity experts graph graph data graph neural network machine machine learning natural network network architecture neural network synthetic via world