Graph Classification Gaussian Processes via Spectral Features
Felix L. Opolka Yin-Cong Zhi Pietro Liò Xiaowen Dong
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.