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.

翻译：图表基因模型是机器学习的一个非常活跃的分支。鉴于日益复杂的新模型的稳步发展,有必要提供一个原则性的方法来评估和比较这些模型。我们在本文件中列举比较指标的可取标准,讨论这些指标的开发,并比较它们各自的表达力。我们系统地评价今天使用的主要指标,突出一些挑战以及研究人员无意中遇到的隐患。然后我们描述一套适当的指标,就它们的实际适用性提出建议,并分析它们在人工生成的扰动图表上的行为以及最近提议的图表基因模型上的行为。