Infection spreading, detection and control in community networks
Community structures widely exist in various complex networks. Extensive studies have been carried out on defining and quantifying community structures as well as developing algorithms for detecting them in extra-large complex systems. Despite all these efforts, however, our understanding of why com...
Saved in:
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/81563 http://hdl.handle.net/10220/43481 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-81563 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-815632020-03-07T13:57:24Z Infection spreading, detection and control in community networks Yu, Yi Xiao, Gaoxi School of Electrical and Electronic Engineering Complex network Community structure Community structures widely exist in various complex networks. Extensive studies have been carried out on defining and quantifying community structures as well as developing algorithms for detecting them in extra-large complex systems. Despite all these efforts, however, our understanding of why community structures widely exist in so many real-life systems, or in other words, the benefits/drawbacks for real-life systems to have community structures, remains to be rather limited. In this work, we discuss on the effects of community structures on infection propagation, detection and control in complex networks. Specifically, we investigate (i) the effects of community structures on transmission speed and infection size; (ii) when monitors can be deployed in the network to detect the infection spreading, the effects of community structures on early-stage infection detection and (iii) in adaptive networks with link rewiring for isolating the infected nodes, the effects of community structures on infection control. Our results show that the existence of community structures generally speaking helps slow down the infection spreading; whether it helps reduce the overall infection size when no control method is adopted however, depends on the network topology. When infection detection and controlling methods such as link rewiring are adopted, the existence of community structures steadily helps improve the efficiency of infection detection and control, though having too many communities may not necessarily bring along additional benefits. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version 2017-07-28T02:07:17Z 2019-12-06T14:33:50Z 2017-07-28T02:07:17Z 2019-12-06T14:33:50Z 2017 Journal Article Yu, Y., & Xiao, G. (2017). Infection spreading, detection and control in community networks. Journal of Complex Networks, 5(4), 625-640. 2051-1310 https://hdl.handle.net/10356/81563 http://hdl.handle.net/10220/43481 10.1093/comnet/cnw031 en Journal of Complex Networks © 2017 The authors (published by Oxford University Press). This is the author created version of a work that has been peer reviewed and accepted for publication in Journal of Complex Networks, published by Oxford University Press on behalf of the authors. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1093/comnet/cnw031]. 17 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
country |
Singapore |
collection |
DR-NTU |
language |
English |
topic |
Complex network Community structure |
spellingShingle |
Complex network Community structure Yu, Yi Xiao, Gaoxi Infection spreading, detection and control in community networks |
description |
Community structures widely exist in various complex networks. Extensive studies have been carried out on defining and quantifying community structures as well as developing algorithms for detecting them in extra-large complex systems. Despite all these efforts, however, our understanding of why community structures widely exist in so many real-life systems, or in other words, the benefits/drawbacks for real-life systems to have community structures, remains to be rather limited. In this work, we discuss on the effects of community structures on infection propagation, detection and control in complex networks. Specifically, we investigate (i) the effects of community structures on transmission speed and infection size; (ii) when monitors can be deployed in the network to detect the infection spreading, the effects of community structures on early-stage infection detection and (iii) in adaptive networks with link rewiring for isolating the infected nodes, the effects of community structures on infection control. Our results show that the existence of community structures generally speaking helps slow down the infection spreading; whether it helps reduce the overall infection size when no control method is adopted however, depends on the network topology. When infection detection and controlling methods such as link rewiring are adopted, the existence of community structures steadily helps improve the efficiency of infection detection and control, though having too many communities may not necessarily bring along additional benefits. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Yu, Yi Xiao, Gaoxi |
format |
Article |
author |
Yu, Yi Xiao, Gaoxi |
author_sort |
Yu, Yi |
title |
Infection spreading, detection and control in community networks |
title_short |
Infection spreading, detection and control in community networks |
title_full |
Infection spreading, detection and control in community networks |
title_fullStr |
Infection spreading, detection and control in community networks |
title_full_unstemmed |
Infection spreading, detection and control in community networks |
title_sort |
infection spreading, detection and control in community networks |
publishDate |
2017 |
url |
https://hdl.handle.net/10356/81563 http://hdl.handle.net/10220/43481 |
_version_ |
1681049294477459456 |