Paper Info
Title
A general Markov chain approach for disease and rumour spreading in complex networks.
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.
Year
DOI
Venue
2018
10.1093/comnet/cnx024
JOURNAL OF COMPLEX NETWORKS
Keywords
Field
DocType
complex networks,Markov chain models,epidemic spreading,rumour propagation
Markov chain,Theoretical computer science,Artificial intelligence,Complex network,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
6
2
2051-1310
Citations 
PageRank 
References 
2
0.37
8
Authors
5
Name
Order
Citations
PageRank
Guilherme Ferraz de Arruda1273.16
Francisco A. Rodrigues212014.16
Pablo Martín Rodriguez3221.63
Emanuele Cozzo4534.16
Yamir Moreno583355.52