Exploring the impact of multilayer networks on dynamic processes in temporal networks
Characterization, analysis, simulation and classification of complex networks
| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Dept Matemat Aplicada & Estatist, Campus Sao Carlos, Caixa Postal 668, BR-13560970 Sao Carlos, SP - Brazil
[2] ISI Fdn, I-10126 Turin - Italy
[3] Univ Warwick, Ctr Complex Sci, Coventry CV4 7AL, W Midlands - England
[4] Univ Warwick, Math Inst, Gibbet Hill Rd, Coventry CV4 7AL, W Midlands - England
[5] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst BIFI, E-50009 Zaragoza - Spain
[6] Univ Zaragoza, Dept Theoret Phys, E-50009 Zaragoza - Spain
Total Affiliations: 6
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| Document type: | Review article |
| Source: | PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS; v. 756, p. 1-59, OCT 5 2018. |
| Web of Science Citations: | 17 |
| Abstract | |
Spreading processes have been largely studied in the literature, both analytically and by means of large-scale numerical simulations. These processes mainly include the propagation of diseases, rumors and information on top of a given population. In the last two decades, with the advent of modern network science, we have witnessed significant advances in this field of research. Here we review the main theoretical and numerical methods developed for the study of spreading processes on complex networked systems. Specifically, we formally define epidemic processes on single and multilayer networks and discuss in detail the main methods used to perform numerical simulations. Throughout the review, we classify spreading processes (disease and rumor models) into two classes according to the nature of time: (i) continuous-time and (ii) cellular automata approach, where the second one can be further divided into synchronous and asynchronous updating schemes. Our revision includes the heterogeneous mean-field, the quenched-mean field, and the pair quenched mean field approaches, as well as their respective simulation techniques, emphasizing similarities and differences among the different techniques. The content presented here offers a whole suite of methods to study epidemic-like processes in complex networks, both for researchers without previous experience in the subject and for experts. (C) 2018 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 12/25219-2 - Modeling, analysis and simulation of dynamic process on complex networks |
| Grantee: | Guilherme Ferraz de Arruda |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 16/25682-5 - Information spreading in complex networks |
| Grantee: | Francisco Aparecido Rodrigues |
| Support Opportunities: | Regular Research Grants |