S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning

arXiv:2605.23467v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often suffer from an information bottleneck when capturing long-range dependencies, leading to the oversquashing (OSQ) phenomenon. Alongside spatial connectivity enrichment (e.g., rewiring), recent studies have shown that spectral filtering can yield strong long-range learning outcomes, as spectral operators enable global information mixing that alleviates OSQ. These approaches achieve this either by stabilizing the Jacobian energies in deep propagation or by guaranteeing OSQ mitigation under strong theoret

Source: arXiv cs.LG — read the full report at the original publisher.

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