With the recent advancements in graph neural networks (GNNs), spectral GNNs
have received increasing popularity by virtue of their specialty in capturing
graph signals in the frequency domain, demonstrating promising capability in
specific tasks. However, few systematic studies have been conducted on
assessing their spectral characteristics. This emerging family of models also
varies in terms of designs and settings, leading to difficulties in comparing
their performance and deciding on the suitable model for specific scenarios,
especially for large-scale tasks. In this work, we extensively benchmark
spectral GNNs with a focus on the frequency perspective. We analyze and
categorize over 30 GNNs with 27 corresponding filters. Then, we implement these
spectral models under a unified framework with dedicated graph computations and
efficient training schemes. Thorough experiments are conducted on the spectral
models with inclusive metrics on effectiveness and efficiency, offering
practical guidelines on evaluating and selecting spectral GNNs with desirable
performance. Our implementation enables application on larger graphs with
comparable performance and less overhead, which is available at:
this https URL

最近在图神经网络领域中，频谱图神经网络因其在频域捕捉图信号的特点而受到广泛关注，展示出在特定任务中的有希望的能力。然而，对于评估其频谱特征的系统研究还很少。此观点论文通过对超过 30 个包含 27 个相应滤波器的频谱图神经网络进行广泛的基准测试，分析和分类这些模型，并在统一框架下实现这些频谱模型，提供了对有效性和效率的多方位实验评估，并提供了在评估和选择性能良好的频谱图神经网络时的实用指南。我们的实现能够在更大的图上应用，性能可比且开销较小，可以从以下链接获得: