On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks
This work addresses the stability and transferability of GNNs for researchers and practitioners, but it is incremental as it builds on existing frameworks and theories.
The authors tackled the problem of understanding the stability of graph neural networks (GNNs) under topological perturbations by analyzing the EdgeNet framework, proving that all GNNs within it are stable and showing that those with lower representational capacity are more stable due to eigenvector misalignment.
Analyzing the stability of graph neural networks (GNNs) under topological perturbations is key to understanding their transferability and the role of each architecture component. However, stability has been investigated only for particular architectures, questioning whether it holds for a broader spectrum of GNNs or only for a few instances. To answer this question, we study the stability of EdgeNet: a general GNN framework that unifies more than twenty solutions including the convolutional and attention-based classes, as well as graph isomorphism networks and hybrid architectures. We prove that all GNNs within the EdgeNet framework are stable to topological perturbations. By studying the effect of different EdgeNet categories on the stability, we show that GNNs with fewer degrees of freedom in their parameter space, linked to a lower representational capacity, are more stable. The key factor yielding this trade-off is the eigenvector misalignment between the EdgeNet parameter matrices and the graph shift operator. For example, graph convolutional neural networks that assign a single scalar per signal shift (hence, with a perfect alignment) are more stable than the more involved node or edge-varying counterparts. Extensive numerical results corroborate our theoretical findings and highlight the role of different architecture components in the trade-off.