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

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

2 Jun 2021ICLR 2022 4arXiv:2106.01098archive 2025-07-28

Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

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 such a comparison metric and provide an overview of the status quo of graph generative model comparison in use today, which predominantly relies on the maximum mean discrepancy (MMD). We perform a systematic evaluation of MMD in the context of graph generative model comparison, highlighting some of the challenges and pitfalls researchers inadvertently may encounter. After conducting a thorough analysis of the behaviour of MMD on synthetically-generated perturbed graphs as well as on recently-proposed graph generative models, we are able to provide a suitable procedure to mitigate these challenges and pitfalls. We aggregate our findings into a list of practical recommendations for researchers to use when evaluating graph generative models.

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