  A wide range of models have been proposed for Graph Generative Models,
necessitating effective methods to evaluate their quality. So far, most
techniques use either traditional metrics based on subgraph counting, or the
representations of randomly initialized Graph Neural Networks (GNNs). We
propose using representations from contrastively trained GNNs, rather than
random GNNs, and show this gives more reliable evaluation metrics. Neither
traditional approaches nor GNN-based approaches dominate the other, however: we
give examples of graphs that each approach is unable to distinguish. We
demonstrate that Graph Substructure Networks (GSNs), which in a way combine
both approaches, are better at distinguishing the distances between graph
datasets.
