Locality-Aware Graph-Rewiring in GNNs
Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein,, Francesco Di Giovanni

TL;DR
This paper introduces a locality-aware graph-rewiring framework for GNNs that reduces over-squashing, respects graph locality, and maintains sparsity, leading to improved performance on real-world benchmarks.
Contribution
The paper proposes a novel locality-aware graph-rewiring method that balances over-squashing reduction, locality preservation, and sparsity, addressing fundamental trade-offs in existing techniques.
Findings
The proposed rewiring framework outperforms existing methods on several benchmarks.
It effectively reduces over-squashing while preserving graph locality.
The approach maintains graph sparsity, enabling scalable GNN training.
Abstract
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to over-squashing, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, graph rewiring techniques have been proposed as a means of improving information flow by altering the graph connectivity. In this work, we identify three desiderata for graph-rewiring: (i) reduce over-squashing, (ii) respect the locality of the graph, and (iii) preserve the sparsity of the graph. We highlight fundamental trade-offs that occur between spatial and spectral rewiring techniques; while the former often…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Functional Brain Connectivity Studies · Advanced Memory and Neural Computing
