Issue |
EPL
Volume 118, Number 6, June 2017
|
|
---|---|---|
Article Number | 68005 | |
Number of page(s) | 7 | |
Section | Interdisciplinary Physics and Related Areas of Science and Technology | |
DOI | https://doi.org/10.1209/0295-5075/118/68005 | |
Published online | 05 September 2017 |
Network repair based on community structure
School of Electronic and Information Engineering, Beihang University - 100191 Beijing, China and Beijing Key Laboratory for Network-based Cooperative ATM - 100191 Beijing, China
Received: 27 February 2017
Accepted: 1 August 2017
Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.
PACS: 89.75.-k – Complex systems / 89.75.Fb – Structures and organization in complex systems / 05.70.Jk – Critical point phenomena
© EPLA, 2017
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.