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This paper addresses the critical oversquashing problem in Graph Neural Networks by introducing an asynchronous message passing framework that improves long-range information propagation without altering graph topology or adding excessive parameters. Theoretically, it demonstrates a slower decay of sensitivity bounds with depth compared to standard synchronous methods, yielding measurable performance gains on complex benchmarks like REDDIT-BINARY and Peptides-struct.
Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
arXiv:2509.06777v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range interactions. Existing remedies are limited: graph rewiring alters edge connectivity, compromising inductive bias, while increasing channel capacity adds parameters. In this work, we propose an efficient, model-agnostic framework that asynchronously updates node features across layers, unlike standard synchronous message passing. At each layer, only a centrality-guided batch of nodes updates, letting information propagate sequentially rather than compress simultaneously into fixed-capacity channels. We show theoretically that our framework's sensitivity bound decays more slowly with depth than synchronous message passing. We have applied the framework to six standard and two long-range graph classification benchmarks, and it achieves notable gains, including $5\%$ and $4\%$ improvements on REDDIT-BINARY and Peptides-struct, respectively.
Abstract:Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range interactions. Existing remedies are limited: graph rewiring alters edge connectivity, compromising inductive bias, while increasing channel capacity adds parameters. In this work, we propose an efficient, model-agnostic framework that asynchronously updates node features across layers, unlike standard synchronous message passing. At each layer, only a centrality-guided batch of nodes updates, letting information propagate sequentially rather than compress simultaneously into fixed-capacity channels. We show theoretically that our framework's sensitivity bound decays more slowly with depth than synchronous message passing. We have applied the framework to six standard and two long-range graph classification benchmarks, and it achieves notable gains, including $5\%$ and $4\%$ improvements on REDDIT-BINARY and Peptides-struct, respectively.
Machine Learning (cs.LG)
arXiv:2509.06777 [cs.LG]
(or arXiv:2509.06777v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2509.06777 Focus to learn more arXiv-issued DOI via DataCite
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