Infection dectection in complex networks with community structures

GAOXI, XIAO and YI, YU (2014) Infection dectection in complex networks with community structures. In: International Conference on Advances in Computer and Electronics Technology - ACET 2014, 26 - 27 August, 2014, City University of Hongkong, Hongkong.

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Abstract

Infection detection is of significant importance as it allows early reaction and proper measures for infection control. Existing studies typically propose algorithms for finding the best locations for a given number of monitors in order to achieve most effective early detection. In this work, we examine the influences of community structures on infection detection. Specifically, a few different cases are tested where monitors are deployed in two community networks, namely community random network and community scale-free network, respectively. By comparing the average/maximum infection sizes in different networks with different community strengths, we show that the existence of community structures, in most cases, helps significantly reduce the infection size. We also test the case where each monitor has a certain probability failing to detect the infection. Simulation results show that in the community networks, similar to that in random networks without community structures, even a low probability of monitor failure may significantly increase the infection size.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: complex network, infection detection, community structure, system reliability
Depositing User: Mr. John Steve
Date Deposited: 28 May 2019 09:26
Last Modified: 28 May 2019 09:26
URI: http://publications.theired.org/id/eprint/2862

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