提出S³GNN模型，有效缓解长程图学习中的信息挤压问题。
S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
- 通过轻量级重构优化信息混合机制，避免复杂理论约束。
- 在多个长程任务上误差降低一个数量级，参数减少50%。
- 适合需要高效长程建模的图神经网络应用，如知识图谱问答。
消息传递神经网络（MPNN）在捕捉长程依赖时常因信息瓶颈导致信息挤压（OSQ）现象。虽然近期研究发现谱滤波能实现强长程学习效果，因其可全局混合信息以缓解OSQ，但这些方法通常依赖于深度传播中雅可比能量稳定的假设或强理论前提。我们重新审视这些结论，发现实际中难以满足此类雅可比敏感性下界。为此，提出S³GNN，通过轻量级重引入被忽略的组件，在计算开销显著降低的前提下，无需严格假设即可缓解OSQ，同时保持特征变换的标准稳定性。在多种场景（如长程基准、知识图谱问答、基于网格的流体动力学）的大量实验表明，S³GNN可实现高达一个数量级的误差降低，且参数量减少最多达50%。代码已开源：https://github.com/EEthanShi/S3-GNN.git。
原文摘要 · Abstract (English)
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 theoretical assumptions. We revisit these conclusions and show that the associated Jacobian sensitivity lower bound is generally difficult to achieve in practice. We then propose S$^3$GNN, which mitigates OSQ without such restrictive assumptions by lightweightly reintroducing omitted components with substantially lower computational complexity, while standard stability constraints on feature transformations remain effective under our new dynamics. Extensive experiments across diverse domains (e.g., long-range benchmarks, KGQA, and mesh-based fluid dynamics) demonstrate that S$^3$GNN achieves up to an order-of-magnitude error reduction with up to 50\% fewer parameters. Our code can be found in https://github.com/EEthanShi/S3-GNN.git.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。