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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A semi-parametric statistical test to compare complex networks

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Author(s):
Fujita, Andre [1] ; Lira, Eduardo Silva [1] ; Santos, Suzana de Siqueira [1] ; Bando, Silvia Yumi [2] ; Soares, Gabriela Eleuterio [1] ; Takahashi, Daniel Yasumasa [3]
Total Authors: 6
Affiliation:
[1] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, Rua Matao 1010, BR-05508090 Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Dept Pediat, Fac Med, Av Dr Eneas Carvalho de Aguiar 647, BR-05403000 Sao Paulo, SP - Brazil
[3] Univ Fed Rio Grande do Norte, Brain Inst, Av Sen Salgado Filho 3000, BR-59077000 Natal, RN - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF COMPLEX NETWORKS; v. 8, n. 2 APR 2020.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 18/17996-5 - Stratification of psychiatric disorders by using network discriminant and clustering analyses
Grantee:André Fujita
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
Grantee:André Fujita
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 15/21162-4 - Identification of variables associated with the graph structure and applications in neuroscience
Grantee:Suzana de Siqueira Santos
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
Grantee:João Eduardo Ferreira
Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants