论文标题
光谱图网络的可传递性的实验研究
An Experimental Study of the Transferability of Spectral Graph Networks
论文作者
论文摘要
光谱图卷积网络是使用拉普拉斯运算符的图形结构数据的标准卷积网络的概括。一个常见的误解是光谱过滤器的不稳定性，即不可能在可变大小和拓扑的图形之间传递光谱过滤器。这种错误的信念限制了用于多刻板任务的光谱网络的开发，而有利于空间图网络。但是，最近的工作证明了光谱扰动下光谱过滤器的稳定性。我们的工作补充并强调了通过对涉及不同大小和连接图的图表进行基准测试光谱图网络的高质量传递性。数值实验在两个图基准的图形回归，图形分类和节点分类问题上表现出良好的性能。我们的实验的实施可在GitHub上获得可重现性。
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.
