Graph classification aims to categorise graphs based on their structure and
node attributes. In this work, we propose to tackle this task using tools from
graph signal processing by deriving spectral features, which we then use to
design two variants of Gaussian process models for graph classification. The
first variant uses spectral features based on the distribution of energy of a
node feature signal over the spectrum of the graph. We show that even such a
simple approach, having no learned parameters, can yield competitive
performance compared to strong neural network and graph kernel baselines. A
second, more sophisticated variant is designed to capture multi-scale and
localised patterns in the graph by learning spectral graph wavelet filters,
obtaining improved performance on synthetic and real-world data sets. Finally,
we show that both models produce well calibrated uncertainty estimates,
enabling reliable decision making based on the model predictions.

本文提出根据图的结构和节点属性对其进行分类的方法，使用图信号处理中的频谱特征派生两种高斯过程模型进行图分类 —— 第一种，基于图谱频谱能量信号分布的频谱特征；第二种，旨在捕捉图中多尺度和局部的模式，通过学习谱图小波滤波器来获得更好的性能。最后，表明二者均产生良好的不确定度估计，可以基于模型预测进行可靠的决策。