Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions

Graph generative models are a highly active branch of machine learning. Given the steady development of new models of ever-increasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this paper, we enumerate the desirable criteria for comparison metrics, discuss the development of such metrics, and provide a comparison of their respective expressive power. We perform a systematic evaluation of the main metrics in use today, highlighting some of the challenges and pitfalls researchers inadvertently can run into. We then describe a collection of suitable metrics, give recommendations as to their practical suitability, and analyse their behaviour on synthetically generated perturbed graphs as well as on recently proposed graph generative models.