Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various graph mining tasks. However, graph Transformer generally treats nodes as tokens, which results in quadratic complexity regarding the number of nodes during self-attention computation. The graph MLP Mixer addresses this challenge by using the efficient MLP Mixer technique from computer vision. However, the time-consuming process of extracting graph tokens limits its performance. In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens. Firstly, we produce multiscale representations of graph nodes via fast Chebyshev polynomial-based spectral filtering. Next, we consider each node's multiscale representations as a sequence of tokens and refine the node representation with an effective MLP Mixer. Finally, we aggregate the multiscale representations of nodes through Chebyshev interpolation. Owing to the powerful representation capabilities and fast computational properties of MLP Mixer, we can quickly extract more informative node representations to improve the performance of downstream tasks. The experimental results prove our significant improvements in a variety of scenarios ranging from graph node classification to medical image segmentation.

翻译：图神经网络在图表示学习领域取得了显著成功，其中图Transformer近期在各种图挖掘任务中展现出卓越性能。然而，图Transformer通常将节点视为标记，这导致自注意力计算存在节点数量的二次复杂度。图MLP Mixer通过采用计算机视觉领域高效的MLP Mixer技术应对这一挑战，但其耗时的图标记提取过程限制了性能。本文提出一种名为ChebMixer的新型架构，这是一种利用快速切比雪夫多项式谱滤波提取标记序列的全新图MLP Mixer。首先，我们通过快速切比雪夫多项式谱滤波生成图节点的多尺度表示。接着，将每个节点的多尺度表示视为标记序列，并利用高效的MLP Mixer优化节点表示。最后，通过切比雪夫插值聚合节点的多尺度表示。凭借MLP Mixer强大的表示能力和快速计算特性，我们能够高效提取信息更丰富的节点表示以提升下游任务性能。实验结果表明，该方法在图节点分类到医学图像分割等多种场景中均取得显著改进。