PISA: Prioritized Invariant Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
PISA addresses a weakness in invariant learning by focusing on how to combine multiple invariant subgraphs. Existing methods use simple averaging, which is suboptimal because it ignores diversity. PISA introduces Prioritized Invariant Subgraph Aggregation, where a lightweight MLP learns an instance-conditional way to weigh the importance of different branches based on the input graph, significantly boosting out-of-distribution performance.