Identifying high betweenness centrality vertices in large noisy networks

Vladimir Ufimtsev, Sanjukta Bhowmick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Most real-world network models inherently include some degree of noise due to the approximations involved in measuring real-world data. My thesis focuses on studying how these approximations affect the stability of the networks. In this paper, we focus on the stability of betweenness centrality (BC), a metric used to measure the importance of the vertices in the network. We present our results on how the ranking of the vertices change as the networks are perturbed and introduce a group testing algorithm that we developed that can correctly identify the high valued BC vertices of stable networks in lower time than the traditional approaches.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013
PublisherIEEE Computer Society
Pages2234-2237
Number of pages4
ISBN (Print)9780769549798
DOIs
StatePublished - Jan 1 2013
Event2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013 - Boston, MA, Japan
Duration: Jul 22 2013Jul 26 2013

Publication series

NameProceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013

Conference

Conference2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013
CountryJapan
CityBoston, MA
Period7/22/137/26/13

Fingerprint

Betweenness
Centrality
Group Testing
Testing
Approximation
Network Model
Ranking
Metric

Keywords

  • Group testing
  • betweenness centrality
  • network noise

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Theoretical Computer Science

Cite this

Ufimtsev, V., & Bhowmick, S. (2013). Identifying high betweenness centrality vertices in large noisy networks. In Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013 (pp. 2234-2237). [6651138] (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013). IEEE Computer Society. https://doi.org/10.1109/IPDPSW.2013.171

Identifying high betweenness centrality vertices in large noisy networks. / Ufimtsev, Vladimir; Bhowmick, Sanjukta.

Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. IEEE Computer Society, 2013. p. 2234-2237 6651138 (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ufimtsev, V & Bhowmick, S 2013, Identifying high betweenness centrality vertices in large noisy networks. in Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013., 6651138, Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013, IEEE Computer Society, pp. 2234-2237, 2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013, Boston, MA, Japan, 7/22/13. https://doi.org/10.1109/IPDPSW.2013.171
Ufimtsev V, Bhowmick S. Identifying high betweenness centrality vertices in large noisy networks. In Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. IEEE Computer Society. 2013. p. 2234-2237. 6651138. (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013). https://doi.org/10.1109/IPDPSW.2013.171
Ufimtsev, Vladimir ; Bhowmick, Sanjukta. / Identifying high betweenness centrality vertices in large noisy networks. Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013. IEEE Computer Society, 2013. pp. 2234-2237 (Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013).
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