A general Markov chain approach for disease and rumour spreading in complex networks

Autor(es)

de Arruda, Guilherme Ferraz ^[1] ; Rodrigues, Francisco Aparecido ^[1] ; Rodriguez, Pablo Martin ^[1] ; Cozzo, Emanuele ^{[2, 3]} ; Moreno, Yamir ^{[2, 3, 4]}

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF COMPLEX NETWORKS; v. 6, n. 2, p. 215-242, APR 2018.
• Citações Web of Science: 5

Resumo

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)

• Processo FAPESP: 16/25682-5 - Propagação de informação em redes complexas
• Beneficiário: Francisco Aparecido Rodrigues
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 15/03868-7 - Comportamento assintótico de processos estocásticos em grafos e aplicações
• Beneficiário: Pablo Martin Rodriguez
• Linha de fomento: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 16/11648-0 - Teoremas limite e resultados de transição de fase para modelos de propagação de informação em grafos
• Beneficiário: Pablo Martin Rodriguez
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/25219-2 - Modelagem, análise e simulação de processos dinâmicos em redes complexas
• Beneficiário: Guilherme Ferraz de Arruda
• Linha de fomento: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/07463-1 - Processos de propagação em redes de múltiplas camadas
• Beneficiário: Guilherme Ferraz de Arruda
• Linha de fomento: Bolsas no Exterior - Estágio de Pesquisa - Doutorado