Spectral graph convolutional networks are generalizations of standard
convolutional networks for graph-structured data using the Laplacian operator.
A common misconception is the instability of spectral filters, i.e. the
impossibility to transfer spectral filters between graphs of variable size and
topology. This misbelief has limited the development of spectral networks for
multi-graph tasks in favor of spatial graph networks. However, recent works
have proved the stability of spectral filters under graph perturbation. Our
work complements and emphasizes further the high quality of spectral
transferability by benchmarking spectral graph networks on tasks involving
graphs of different size and connectivity. Numerical experiments exhibit
favorable performance on graph regression, graph classification, and node
classification problems on two graph benchmarks. The implementation of our
experiments is available on GitHub for reproducibility.

基于 Laplacian 算子，谱图卷积神经网络是一种用于图数据的卷积网络，并已被证明可以稳定地在不同大小和连接性的图之间传输谱滤波器，在图回归、图分类和节点分类等任务中表现出良好的性能。

谱图网络可迁移性的实验研究

An Experimental Study of the Transferability of Spectral Graph Networks

Spectral Graph Convolutional Networks (GCNs) are a generalization of
convolutional networks to learning on graph-structured data. Applications of
spectral GCNs have been successful, but limited to a few problems where the
graph is fixed, such as shape correspondence and node classification. In this
work, we address this limitation by revisiting a particular family of spectral
graph networks, Chebyshev GCNs, showing its efficacy in solving graph
classification tasks with a variable graph structure and size. Chebyshev GCNs
restrict graphs to have at most one edge between any pair of nodes. To this
end, we propose a novel multigraph network that learns from multi-relational
graphs. We model learned edges with abstract meaning and experiment with
different ways to fuse the representations extracted from annotated and learned
edges, achieving competitive results on a variety of chemical classification
benchmarks.

本文探讨了谱图卷积网络（GCNs）在变量图结构和大小的情况下解决图分类任务的效力，提出了一种从多关系图中学习的新型多图网络，并成功地在多个化学分类基准测试中取得了竞争性的结果。