Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5632
Title: Attack resilience of the evolving scientific collaboration network
Authors: Liu, XF
Xu, X
Small, M
Tse, CKM 
Issue Date: 14-Oct-2011
Publisher: Public Library of Science
Source: PLoS one, 14 Oct. 2011, v. 6, no. 10, e26271, p.1-7 How to cite?
Journal: PLoS one 
Abstract: Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local ("microscopic") level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level - fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks.
URI: http://hdl.handle.net/10397/5632
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0026271
Rights: © 2011 Liu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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