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This paper proposes PairAlign, a pair-centric graph rewiring framework that uses optimal transport-guided communication alignment to alleviate over-squashing in message-passing neural networks, with theoretical analysis and experiments on standard benchmarks.
Introduces Schreier-Coset Graph Rewiring, a group-theoretic method to rewire graphs for GNNs, mitigating over-squashing by improving spectral gap and effective resistance. Empirical results show significant reduction in effective resistance across learning tasks.