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Sink Group Betweenness Centrality

Published:07 September 2021Publication History

ABSTRACT

This article introduces the concept of Sink Group Node Betweenness centrality to identify those nodes in a network that can “monitor” the geodesic paths leading towards a set of subsets of nodes; it generalizes both the traditional node betweenness centrality and the sink betweenness centrality. We also provide extensions of the basic concept for node-weighted networks, and also describe the dual notion of Sink Group Edge Betweenness centrality. We exemplify the merits of these concepts and describe some areas where they can be applied.

References

  1. G. A. A. Akanmu, F. Z. Wang, and H. Chen. 2012. Introducing weighted nodes to evaluate the cloud computing topology. Journal of Software Engineering & Applications 5, 11(2012), 961–969.Google ScholarGoogle ScholarCross RefCross Ref
  2. D. A. Bader, S. Kintali, K. Madduri, and M. Mihail. 2007. Approximating betweenness centrality. In Proceedings of the International Workshop on Algorithms & Models for the Web-Graph (WAW). 124–137.Google ScholarGoogle Scholar
  3. A. L. Barabasi. 2016. Network Science. Cambridge University Press.Google ScholarGoogle Scholar
  4. A. L. Barabasi and R. Albert. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509–512.Google ScholarGoogle Scholar
  5. P. Basaras, G. Iosifidis, D. Katsaros, and L. Tassiulas. 2019. Identifying influential spreaders in complex multilayer networks: A centrality perspective. IEEE Transactions on Network Science & Engineering 6, 1(2019), 31–45.Google ScholarGoogle ScholarCross RefCross Ref
  6. P. Basaras, D. Katsaros, and L. Tassiulas. 2013. Detecting influential spreaders in complex, dynamic networks. IEEE Computer Magazine 46, 4 (2013), 26–31.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. P. Basaras, D. Katsaros, and L. Tassiulas. 2015. Dynamically blocking contagions in complex networks by cutting vital connections. In Proceedings of the IEEE International Conference on Communications (ICC). 1170–1175.Google ScholarGoogle Scholar
  8. X. Bei, W. Chen, S.-H. Teng, J. Zhang, and J. Zhu. 2011. Bounded budget betweenness centrality game for strategic network formations. Theoretical Computer Science 412 (2011), 7147–7168.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. G. Bianconi. 2018. Multilayer Networks: Structure and Function. Oxford University Press.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. G. Bonchi, F. De Francisci and Riondato M.2016. Centrality measures on big graphs: Exact, approximated, and distributed algorithms. In Proceedings of the ACM International Conference on the World Wide Web (WWW). 1017–1020.Google ScholarGoogle Scholar
  11. J. Borge-Holthoefer and Y. Moreno. 2012. Absence of influential spreaders in rumor dynamics. Physical Review E 85, 2 (2012).Google ScholarGoogle ScholarCross RefCross Ref
  12. U. Brandes. 2001. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 2 (2001), 163–177.Google ScholarGoogle Scholar
  13. U. Brandes. 2008. On variants of shortest-path betweenness centrality and their generic computation. Social Networks 30, 2 (2008), 136–145.Google ScholarGoogle ScholarCross RefCross Ref
  14. M. H. Chehreghani. 2014. Effective co-betweenness centrality computation. In Proceedings of the ACM Conference on Web Search & Data Mining (SDM). 423–432.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Cuzzocrea, A. Papadimitriou, D. Katsaros, and Y. Manolopoulos. 2012. Edge betweenness centrality: A novel algorithm for QoS-based topology control over wireless sensor networks. Journal of Network & Computer Applications 35, 4 (2012), 1210–1217.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Dolev, Y. Elovici, and R. Puzis. 2010. Routing betweenness centrality. J. ACM 57, 4 (2010).Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Dolev, Y. Elovici, R. Puzis, and P. Zilberman. 2009. Incremental deployment of network monitors based on Group Betweenness Centrality. Inform. Process. Lett. 109 (2009), 1172–1176.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. Easley and J. Kleinberg. 2010. Networks, Crowds and Markets: Reasoning About a Highly Connected World. Cambridge University Press.Google ScholarGoogle Scholar
  19. M. G. Everett and S. P. Borgatti. 1999. The centrality of groups and classes. Journal of Mathematical Sociology 23, 3 (1999), 181–201.Google ScholarGoogle ScholarCross RefCross Ref
  20. M. G. Everett and S. P. Borgatti. 2009. The centrality of groups and classes. The Journal of Mathematical Sociology 23, 3 (2009), 181–201.Google ScholarGoogle ScholarCross RefCross Ref
  21. S. Fortunato. 2010. Community detection in graphs. Physics Reports 486, 3-5 (2010), 75–174.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Fortunato and D. Hric. 2016. Community detection in networks: A user guide. Physics Reports 659(2016), 1–44.Google ScholarGoogle ScholarCross RefCross Ref
  23. L. C. Freeman. 1977. A set of measures of centrality based on betweenness. Sociometry 40, 1 (1977), 35–41.Google ScholarGoogle ScholarCross RefCross Ref
  24. R. Geisberger, P. Sanders, and D. Schultes. 2008. Better approximation of betweenness centrality. In Proceedings of the Meeting on Algorithm Engineering & Experiments (ALENEX). 90–100.Google ScholarGoogle Scholar
  25. M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99 (2002), 7821–7826.Google ScholarGoogle ScholarCross RefCross Ref
  26. A. Halu, R. J. Mondragón, P. Panzarasa, and G. Bianconi. 2013. Multiplex PageRank. PLOS One 8, 10 (2013), e78293.Google ScholarGoogle ScholarCross RefCross Ref
  27. X. Huang, D. Chen, T. Ren, and D. Wang. 2021. A survey of community detection methods in multilayer networks. Data Mining & Knowledge Discovery 35 (2021), 1–45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. W. Hwang, T. Kim, M. Ramanathan, and A. Zhang. 2008. Bridging centrality: Graph mining from element level to group level. In Proceedings of the ACM Conference on Knowledge Discovery & Data Mining (KDD). 336–344.Google ScholarGoogle Scholar
  29. D. Katsaros and P. Basaras. 2015. Detecting influential nodes in complex networks with range probabilistic control centrality. In Coordination Control of Distributed Systems, J. H. van Schuppen and T. Villa (Eds.). Lecture Notes in Control and Information Sciences, Vol. 456. Springer-Verlag, 265–272.Google ScholarGoogle Scholar
  30. D. Katsaros, N. Dimokas, and L. Tassiulas. 2010. Social network analysis concepts in the design of wireless ad hoc network. IEEE Network Magazine 24, 6 (2010).Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. 2010. Identification of influential spreaders in complex networks. Nature Physics 6(2010), 888–893.Google ScholarGoogle ScholarCross RefCross Ref
  32. E. D. Kolaczyk, D. B. Chua, and M. Barthelemy. 2009. Group betweenness and co-betweenness: Inter-related notions of coalition centrality. Social Networks 31, 3 (2009), 190–203.Google ScholarGoogle ScholarCross RefCross Ref
  33. A. N. Langville and C. D. Meyer. 2006. Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. V. Lattora, V. Nicosia, and G. Russo. 2017. Complex Networks: Principles, Methods and Applications. Cambridge University Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. L. Li, G. Wang, and M. Yu. 2015. Pairwise co-betweenness for several types of network. Journal of Networks 10, 2 (2015), 91–98.Google ScholarGoogle ScholarCross RefCross Ref
  36. L. Maccari, L. Ghiro, A. Guerrieri, A. Montresor, and R. Lo Cigno. 2020. Exact distributed load centrality computation: Algorithms, convergence, and applications to distance vector routing. IEEE Transactions on Parallel & Distributed Systems 31, 7 (2020), 1693–1706.Google ScholarGoogle ScholarCross RefCross Ref
  37. N. Magaia, A. P. Francisco, P. Pereira, and M. Correia. 2015. Betweenness centrality in Delay Tolerant Networks: A survey. Ad Hoc Networks 33(2015), 284–305.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. P. Mahendra, P. Mikhail, and H. Liaquat. 2013. Percolation centrality: Quantifying graph-theoretic impact of nodes during percolation in networks. PLOS One 8, 1 (2013).Google ScholarGoogle Scholar
  39. N. Masuda and R. Lambiotte. 2016. A Guide to Temporal Networks. Series on Complexity Science, Vol. 4. World Scientific.Google ScholarGoogle Scholar
  40. J. Matta, G. Ercal, and K. Sinha. 2019. Comparing the speed and accuracy of approaches to betweenness centrality approximation. Computational Social Networks 6 (2019).Google ScholarGoogle Scholar
  41. M. E. J. Newman. 2010. Networks: An Introduction. Oxford University Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. P. Pantazopoulos, M. Karaliopoulos, and I. Stavrakakis. 2014. Distributed placement of autonomic Internet services. IEEE Transactions on Parallel & Distributed Systems 25, 7 (2014), 1702–1712.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. D. Papakostas, T. Kasidakis, E. Fragkou, and D. Katsaros. 2021. Backbones for Internet of Battlefield Things. In Proceedings of the IEEE/IFIP Conference on Wireless On-demand Network Systems & Services (WONS). 116–123.Google ScholarGoogle Scholar
  44. R. Puzis, Y. Elovici, and S. Dolev. 2007. Fast algorithm for successive computation of group betweenness centrality. Physical Review E 76(2007), 056709.Google ScholarGoogle ScholarCross RefCross Ref
  45. H. S. Ramos, A. C. Frery, A. Boukerche, E. M. R. Oliveira, and A. A. F. Loureiro. 2014. Topology-related metrics and applications for the design and operation of wireless sensor networks. ACM Transactions on Sensor Networks 10, 3 (2014), 1–35.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. G. Seyfang and N. N. Longhurst. 2013. Growing green money? Mapping community currencies for sustainable development. Ecological Economics 86(2013), 65–77.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    IDEAS '21: Proceedings of the 25th International Database Engineering & Applications Symposium
    July 2021
    308 pages
    ISBN:9781450389914
    DOI:10.1145/3472163

    Copyright © 2021 ACM

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    New York, NY, United States

    Publication History

    • Published: 7 September 2021

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    Overall Acceptance Rate74of210submissions,35%

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