Degree‐based goodness‐of‐fit tests for heterogeneous random graph models: Independent and exchangeable cases. (29th October 2019)

• Record Type: Journal Article
• Authors: Ouadah, Sarah; Robin, Stéphane; Latouche, Pierre
• Is Part Of: Scandinavian journal of statistics. Volume 47:Number 1(2020:Mar.)
• Page Start: 156
• Page End: 181
• Publication Date: 2019-10-29
• Subjects: degree variance -- goodness of fit -- graphon -- random graphs -- W‐graph; Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12410
• Languages: English
• ISSNs: 0303-6898

Abstract

Abstract: The degrees are a classical and relevant way to study the topology of a network. They can be used to assess the goodness of fit for a given random graph model. In this paper, we introduce goodness‐of‐fit tests for two classes of models. First, we consider the case of independent graph models such as the heterogeneous Erdös‐Rényi model in which the edges have different connection probabilities. Second, we consider a generic model for exchangeable random graphs called the W ‐graph. The stochastic block model and the expected degree distribution model fall within this framework. We prove the asymptotic normality of the degree mean square under these independent and exchangeable models and derive formal tests. We study the power of the proposed tests and we prove the asymptotic normality under specific sparsity regimes. The tests are illustrated on real networks from social sciences and ecology, and their performances are assessed via a simulation study.