A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks

Abstract

Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achieving state-of-the-art results on node and graph classification tasks. The proposed GNNs, however, often implement complex node and graph embedding schemes, which makes it challenging to explain their performance. In this paper, we investigate the link between a GNN’s expressiveness, that is, its ability to map different graphs to different representations, and its generalization performance in a graph classification setting. In particular, we propose a principled experimental procedure where we (i) define a practical measure for expressiveness, (ii) introduce an expressiveness-based loss function that we use to train a simple yet practical GNN that is permutation-invariant, (iii) illustrate our procedure on benchmark graph classification problems and on an original real-world application. Our results reveal that expressiveness alone does not guarantee a better performance, and that a powerful GNN should be able to produce graph representations that are well separated with respect to the class of the corresponding graphs.