提出新型图波网络，有效捕捉长距离图结构信息。
Long-Range Graph Wavelet Networks
- 将图波滤波器分解为局部与全局组件，分别用低阶多项式和灵活谱参数化处理。
- 在长程基准测试中表现优于现有波网方法，短程任务也保持竞争力。
- 适合需要建模远距离依赖的图学习场景，如社交网络、分子结构分析。
建模长距离交互（即信息在图中远端部分的传播）是图机器学习的核心挑战。图波变换借鉴多分辨率信号处理思想，能同时捕捉局部与全局结构。然而，现有基于波的图神经网络依赖有限阶多项式近似，限制了感受野并阻碍长距离信息传播。我们提出长距离图波网络（LR-GWN），将波滤波器分解为互补的局部与全局成分：局部聚合采用高效低阶多项式，长距离交互则通过灵活的谱域参数化实现。该混合设计在统一的波理论框架下融合短程与长程信息流。实验表明，LR-GWN在长距离基准上达到波网方法的最先进性能，同时在短距离数据集上仍具竞争力。
原文摘要 · Abstract (English)
Modeling long-range interactions, the propagation of information across distant parts of a graph, is a central challenge in graph machine learning. Graph wavelets, inspired by multi-resolution signal processing, provide a principled way to capture both local and global structures. However, existing wavelet-based graph neural networks rely on finite-order polynomial approximations, which limit their receptive fields and hinder long-range propagation. We propose Long-Range Graph Wavelet Networks (LR-GWN), which decompose wavelet filters into complementary local and global components. Local aggregation is handled with efficient low-order polynomials, while long-range interactions are captured through a flexible spectral-domain parameterization. This hybrid design unifies short- and long-distance information flow within a principled wavelet framework. Experiments show that LR-GWN achieves state-of-the-art performance among wavelet-based methods on long-range benchmarks, while remaining competitive on short-range datasets.
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