- TL;DR: The paper presents a set of experiements which highlight the gap in our intuitive understanding of Graph Neural Networks.
- Abstract: We highlight a lack of understanding of the behaviour of Graph Neural Networks (GNNs) in various topological contexts. We present 4 experimental studies which counter-intuitively demonstrate that the performance of GNNs is weakly dependent on the topology, sensitive to structural noise and the modality (attributes or edges) of information, and degraded by strong coupling between nodal attributes and structure. We draw on the empirical results to recommend reporting of topological context in GNN evaluation and propose a simple (attribute-structure) decoupling method to improve GNN performance.
- Keywords: Graph Neural Networks, Graph Toplogy, Noise, Attributed Networks
- Original Pdf: pdf