MassExodus: modeling evolving networks in harsh environments

Navlakha, S., Faloutsos, C., Bar-Joseph, Z. (September 2015) MassExodus: modeling evolving networks in harsh environments. Data Mining and Knowledge Discovery, 29 (5). pp. 1211-1232. ISSN 13845810 (ISSN)

DOI: 10.1007/s10618-014-0399-1


Consider networks in harsh environments, where nodes may be lost due to failure, attack, or infection-how is the topology affected by such events? Can we mimic and measure the effect? We propose a new generative model of network evolution in dynamic and harsh environments. Our model can reproduce the range of topologies observed across known robust and fragile biological networks, as well as several additional transport, communication, and social networks. We also develop a new optimization measure to evaluate robustness based on preserving high connectivity following random or adversarial bursty node loss. Using this measure, we evaluate the robustness of several real-world networks and propose a new distributed algorithm to construct secure networks operating within malicious environments. © 2014, The Author(s).

Item Type: Paper
Additional Information: Data Min. Knowl. Discov.
Uncontrolled Keywords: Biological fragility Graph models Network robustness Biology Biological networks Graph model Harsh environment High connectivity Optimization measures Real-world networks Topology
Subjects: bioinformatics > computational biology > algorithms
bioinformatics > computational biology
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 22 September 2015
Date Deposited: 06 Nov 2019 20:28
Last Modified: 06 Nov 2019 20:28
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