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Title: A semi-parametric statistical test to compare complex networks
Authors: Fujita, Andre
Lira, Eduardo Silva
Santos, Suzana de Siqueira
Bando, Silvia Yumi
Soares, Gabriela Eleuterio
Takahashi, Daniel Yasumasa
Keywords: Random graph;parameter estimation;model selection;ANOVA;graph spectrum;isomorphism
Issue Date: 2-Aug-2019
Citation: FUJITA, A.; LIRA, E. S.; SANTOS, S. S.; BANDO, S. Y.; SOARES, G. E.; TAKAHASHI, D. Y. A semi-parametric statistical test to compare complex networks. Journal of Complex Networks, [s. l.], p. 1-17, ago. 2019. DOI: Disponível em: Acesso em: 04 set. 2019.
Portuguese Abstract: The modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graphs, real-world networks are better modelled as realizations of a random process. Therefore, analyses using methods based on deterministic graphs may be inappropriate. For example, verifying the isomorphism between two graphs is of limited use to decide whether two (or more) real-world networks are generated from the same random process. To overcome this problem, in this article, we introduce a semi-parametric approach similar to the analysis of variance to test the equality of generative models of two or more complex networks. We measure the performance of the proposed statistic using Monte Carlo simulations and illustrate its usefulness by comparing PPI networks of six enteric pathogens.
Appears in Collections:ICe - Artigos publicados em periódicos

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