Stochastic models for the spreading of rumours and epidemics
Analysis of epidemic and synchronization processes in complex networks
| Full text | |
| Author(s): |
de Arruda, Guilherme Ferraz
[1]
;
Rodrigues, Francisco Aparecido
[1]
;
Rodriguez, Pablo Martin
[1]
;
Cozzo, Emanuele
[2, 3]
;
Moreno, Yamir
[2, 3, 4]
Total Authors: 5
|
| Affiliation: | [1] Univ Sao Paulo, Dept Matemat Aplicada & Estat, Inst Ciencias Matemat & Comp, Campus Sao Carlos, Caixa Postal 668, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Zaragoza, Inst Biocomputat & Phys Complex Syst BIFI, Zaragoza 50018 - Spain
[3] Univ Zaragoza, Dept Theoret Phys, Zaragoza 50018 - Spain
[4] Inst Sci Interchange, Complex Networks & Syst Lagrange Lab, Turin - Italy
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | JOURNAL OF COMPLEX NETWORKS; v. 6, n. 2, p. 215-242, APR 2018. |
| Web of Science Citations: | 5 |
| Abstract | |
Spreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumour spreading are among the most important of these processes due to their practical relevance. However, despite the similarities between them, current models address both spreading dynamics separately. In this article, we propose a general spreading model that is based on discrete time Markov chains. The model includes all the transitions that are plausible for both a disease contagion process and rumour propagation. We show that our model not only covers the traditional spreading schemes but that it also contains some features relevant in social dynamics, such as apathy, forgetting, and lost/recovering of interest. The model is evaluated analytically to obtain the spreading thresholds and the early time dynamical behaviour for the contact and reactive processes in several scenarios. Comparison with Monte Carlo simulations shows that the Markov chain formalism is highly accurate while it excels in computational efficiency. We round off our work by showing how the proposed framework can be applied to the study of spreading processes occurring on social networks. (AU) | |
| FAPESP's process: | 15/07463-1 - Spreading processes on multilayer networks |
| Grantee: | Guilherme Ferraz de Arruda |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| 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: | 16/11648-0 - Limit theorems and phase transition results for information propagation models on graphs |
| Grantee: | Pablo Martin Rodriguez |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 15/03868-7 - Asymptotic behavior of stochastic processes on graphs and applications |
| Grantee: | Pablo Martin Rodriguez |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 16/25682-5 - Information spreading in complex networks |
| Grantee: | Francisco Aparecido Rodrigues |
| Support Opportunities: | Regular Research Grants |