提出快速求解隐式图神经网络的新方法，显著提升大图推理速度。
IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
- 用参数化图神经网络加速固定点迭代求解
- 在不损失精度下实现1.5到8倍推理提速
- 适合需要高效处理大规模图的应用场景
隐式图神经网络（IGNNs）虽具强大表达能力且能有效缓解过平滑问题，但依赖计算开销大的固定点迭代，严重制约其在大规模图上的应用。为此，我们提出IGNN-Solver，利用广义Anderson加速方法，通过一个小型图神经网络参数化迭代更新过程，将求解建模为图相关的时序动态过程。为进一步提升在大规模图任务上的表现，我们在设计中集成专为IGNN-Solver定制的稀疏化与存储压缩技术。大量实验表明，IGNN-Solver在小规模和大规模任务上均显著加速推理，在不牺牲准确率的前提下实现1.5×至8×的速度提升，且随着图规模增大优势更明显，为真实场景的大规模部署提供了可能。代码已公开于https://github.com/landrarwolf/IGNN-Solver。
原文摘要 · Abstract (English)
Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range dependencies (LRD) in underlying graphs while effectively mitigating the over-smoothing problem. However, IGNNs rely on computationally expensive fixed-point iterations, which lead to significant speed and scalability limitations, hindering their application to large-scale graphs. To achieve fast fixed-point solving for IGNNs, we propose a novel graph neural solver, IGNN-Solver, which leverages the generalized Anderson Acceleration method, parameterized by a tiny GNN, and learns iterative updates as a graph-dependent temporal process. To improve effectiveness on large-scale graph tasks, we further integrate sparsification and storage compression methods, specifically tailored for the IGNN-Solver, into its design. Extensive experiments demonstrate that the IGNN-Solver significantly accelerates inference on both small- and large-scale tasks, achieving a $1.5\times$ to $8\times$ speedup without sacrificing accuracy. This advantage becomes more pronounced as the graph scale grows, facilitating its large-scale deployment in real-world applications. The code to reproduce our results is available at https://github.com/landrarwolf/IGNN-Solver.
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