Advertisement

Social Network Analysis and Mining

, Volume 3, Issue 4, pp 899–914 | Cite as

Identifying high betweenness centrality nodes in large social networks

  • Nicolas KourtellisEmail author
  • Tharaka Alahakoon
  • Ramanuja Simha
  • Adriana Iamnitchi
  • Rahul Tripathi
Original Article
First Online:

Abstract

This paper proposes an alternative way to identify nodes with high betweenness centrality. It introduces a new metric, κ-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high κ-path centrality have high node betweenness centrality. The randomized algorithm runs in time O(κ3 n 2−2αlog n) and outputs, for each vertex v, an estimate of its κ-path centrality up to additive error of ±n 1/2+α with probability 1 − 1/n 2. Experimental evaluations on real and synthetic social networks show improved accuracy in detecting high betweenness centrality nodes and significantly reduced execution time when compared with existing randomized algorithms.

Keywords

Betweenness centrality Social network analysis Algorithms Experimental evaluation 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This research was partially supported by the National Science Foundation under Grants No. CNS-0831785 and CNS-0952420. The authors would also like to acknowledge the use of the computing services provided by Research Computing, University of South Florida.

References

  1. Alahakoon T, Tripathi R, Kourtellis N, Simha R, Iamnitchi A (2011) K-path centrality: a new centrality measure in social networks. In: 4th ACM EuroSys workshop on social network systemsGoogle Scholar
  2. Ang CS (2011) Interaction networks and patterns of guild community in massively multiplayer online games. Soc Netw Anal Min 1(4):341–353CrossRefGoogle Scholar
  3. Anthonisse J (1971) The rush in a directed graph. Technical Report BN9/71. Stichting Mathematisch Centrum, AmsterdamGoogle Scholar
  4. Bader D, Kintali S, Madduri K, Mihail M (2007) Approximating betweenness centrality. In: 5th Workshop on algorithms and models for the web-graph, pp 124–137Google Scholar
  5. Batagelj V, Mrvar A (2006) Pajek datasets. http://vlado.fmf.uni-lj.si/pub/networks/data/
  6. Blackburn J, Simha R, Kourtellis N, Zuo X, Ripeanu M, Skvoretz J, Iamnitchi A (2012) Branded with a scarlet C: cheaters in a gaming social network. In: 21st International conference on world wide web, Lyon, FranceGoogle Scholar
  7. Borgatti S, Everett M (2006) A graph-theoretic perspective on centrality. Soc Netw 28(4):466–484CrossRefGoogle Scholar
  8. Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177CrossRefzbMATHGoogle Scholar
  9. Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Netw 30(2):136–145MathSciNetCrossRefGoogle Scholar
  10. Brandes U, Fleischer D (2005) Centrality measures based on current flow. In: Proceedings of the 22nd annual symposium on theoretical aspects of computer science, Lecture notes in computer science, vol 3404. Springer, pp 533–544Google Scholar
  11. Brandes U, Pich C (2007) Centrality estimation in large networks. Int J Bifurc Chaos 17(7):2303–2318 (Special Issue on Complex Networks Structure and Dynamics)MathSciNetCrossRefzbMATHGoogle Scholar
  12. Catanese S, Ferrara E, Fiumara G (2012) Forensic analysis of phone call networks. Soc Netw Anal Min. doi: 10.1007/s13278-012-0060-1
  13. Eppstein D, Wang J (2004) Fast approximation of centrality. J Graph Algorithms Appl 8(1):39–45MathSciNetCrossRefzbMATHGoogle Scholar
  14. Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41CrossRefGoogle Scholar
  15. Freeman C, Borgatti S, White D (1991) Centrality in valued graphs: A measure of betweenness based on network flow. Soc Netw 13(2):141–154MathSciNetCrossRefGoogle Scholar
  16. Friedkin N (1983) Horizons of observability and limits of informal control in organizations. Soc Forces, 62(1):57–77Google Scholar
  17. Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58:13–30MathSciNetCrossRefzbMATHGoogle Scholar
  18. Hua G, Haughton D (2012) A network analysis of an online expertise sharing community. Soc Netw Anal Min. doi: 10.1007/s13278-012-0047-y
  19. Iamnitchi A, Ripeanu M, Foster I (2004) Small-world file-sharing communities. In: 23rd Conf. of the IEEE Communications Society (InfoCom), pp 952–963Google Scholar
  20. Jacob R, Koschützki D, Lehmann K, Peeters L, Podehl D (2005) Algorithms for centrality indices. In Network Analysis, volume 3418 of LNCS, Springer, pp 62–82Google Scholar
  21. Jeong H, Mason S, Barabási A, Oltvai Z (2001) Lethality and centrality in protein networks. Nature 411:41CrossRefGoogle Scholar
  22. Kahng G, Oh E, Kahng B, Kim D (2003) Betweenness centrality correlation in social networks. Phys Rev E 67:01710–01711Google Scholar
  23. Kourtellis N, Iamnitchi A (2011) Inferring peer centrality in socially-informed peer-to-peer systems. In: 11th IEEE International conference on Peer-to-Peer computing, Kyoto, JapanGoogle Scholar
  24. Leskovec J (2011) Stanford large network dataset collection. http://snap.stanford.edu/data/
  25. Liljeros F, Edling C, Amaral L, Stanley H, Aberg Y (2001) The web of human sexual contacts. Nature 411:907CrossRefGoogle Scholar
  26. Lipton R, Naughton J (1989) Estimating the size of generalized transitive closures. In: 15th International conference on very large databases. Morgan Kaufmann, San Francisco, pp 165–171Google Scholar
  27. Macskassy S (2011) Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis. Soc Netw Anal Min 1(4):355–375CrossRefGoogle Scholar
  28. Maglaras LA, Katsaros D (2011) New measures for characterizing the significance of nodes in wireless ad hoc networks via localized path-based neighborhood analysis. Soc Netw Anal Min. doi: 10.1007/s13278-011-0029-5
  29. Newman M (2001) The structure of scientific collaboration networks. Proc Nat Acad Sci USA 98(2):404–409CrossRefzbMATHGoogle Scholar
  30. Newman M (2005) A measure of betweenness centrality based on random walks. Soc Netw 27(1):39–54CrossRefGoogle Scholar
  31. Ortiz M, Hoyos J, Lopez M (2004) The social networks of academic performance in a student context of poverty in Mexico. Soc Netw 26(2):175–188CrossRefGoogle Scholar
  32. Ripeanu M, Iamnitchi A, Foster I (2002) Mapping the Gnutella network. Internet Comput IEEE 6(1):50–57CrossRefGoogle Scholar
  33. Said Y, Wegman E, Sharabati W, Rigsby J (2008) Social networks of author-coauthor relationships. Comput Stat Data Anal 52:2177–2184MathSciNetCrossRefGoogle Scholar
  34. Sala A, Cao L, Wilson C, Zablit R, Zheng H, Zhao B (2010) Measurement-calibrated graph models for social network experiments. In: 19th International conference on world wide web, pp 861–870Google Scholar
  35. Singh B, Gupte N (2005) Congestion and decongestion in a communication network. Phys Rev E 71(5):055103CrossRefGoogle Scholar
  36. Stephenson K, Zelen M (1989) Rethinking centrality: methods and examples. Soc Netw 11:1–37MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Nicolas Kourtellis
    • 1
    Email author
  • Tharaka Alahakoon
    • 2
  • Ramanuja Simha
    • 3
  • Adriana Iamnitchi
    • 1
  • Rahul Tripathi
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.NorcrossUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of DelawareNewarkUSA

Personalised recommendations

Cite article

Buy options