When Are Graph Neural Networks Better Than Structure-Agnostic Methods?

Diana Gomes, Frederik Ruelens, Kyriakos Efthymiadis, Ann Nowe, Peter Vrancx

Keywords: graph neural networks, empiric falsification, graph properties

TL;DR: An expansion on GNN failure cases by decoupling the impact of structural and feature information on the learning process.

Abstract: Graph neural networks (GNNs) are commonly applied to graph data, but their performance is often poorly understood. It is easy to find examples in which a GNN is unable to learn useful graph representations, but generally hard to explain why. In this work, we analyse the effectiveness of graph representations learned by shallow GNNs (2-layers) for input graphs with different structural properties and feature information. We expand on the failure cases by decoupling the impact of structural and feature information on the learning process. Our results indicate that GNNs' implicit architectural assumptions are tightly related to the structural properties of the input graph and may impair its learning ability. In case of mismatch, they can often be outperformed by structure-agnostic methods like multi-layer perceptron.