Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Authors: Chai, W.

http://eprints.bournemouth.ac.uk/29883/

Journal: IEEE Transactions on Network Science and Engineering

Publisher: IEEE

DOI: 10.1109/TNSE.2017.2764523

Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents’ vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters’ activities in the system. We highlight the key differences in the spreading patterns between the contact-based vs. agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based model due to agents’ movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.

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Authors: Chai, W.K.

http://eprints.bournemouth.ac.uk/29883/

Journal: IEEE Transactions on Network Science and Engineering

Volume: 5

Issue: 4

Pages: 336-349

eISSN: 2327-4697

DOI: 10.1109/TNSE.2017.2764523

© 2013 IEEE. Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents' vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters' activities in the system. We highlight the key differences in the spreading patterns between the contact-based versus agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based models due to agents' movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.

This data was imported from Web of Science (Lite):

Authors: Chai, W.K.

http://eprints.bournemouth.ac.uk/29883/

Journal: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

Volume: 5

Issue: 4

Pages: 336-349

ISSN: 2327-4697

DOI: 10.1109/TNSE.2017.2764523

The data on this page was last updated at 05:17 on May 25, 2020.