Generalization and Representational Limits of Graph Neural Networks
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3419-3430, 2020.
We address two fundamental questions about graph neural networks (GNNs). First, we prove that several important graph properties, e.g., shortest/longest cycle, diameter, or certain motifs, cannot be computed by GNNs that rely entirely on local information. Such GNNs include the standard message passing models, and more powerful spatial variants that exploit local graph structure (e.g., via relative orientation of messages, or local port ordering) to distinguish neighbors of each node. Our treatment includes a novel graph-theoretic formalism. Second, we provide the first data dependent generalization bounds for message passing GNNs. This analysis explicitly accounts for the local permutation invariance of GNNs. Our bounds are much tighter than existing VC-dimension based guarantees for GNNs, and are comparable to Rademacher bounds for recurrent neural networks.