Learnable random-walk path sampling with a kernel-density mutual information loss improves node classification under distribution shifts, outperforming prior graph OOD methods on seven benchmarks.
Investigating out-of-distribution generalization of gnns: An architecture perspective
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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
Learnable random-walk path sampling with a kernel-density mutual information loss improves node classification under distribution shifts, outperforming prior graph OOD methods on seven benchmarks.