Skip to main content

A Method for Identifying Bridges in Online Social Networks

  • Conference paper
  • First Online:
Recent Trends in Analysis of Images, Social Networks and Texts (AIST 2021)

Abstract

The current level of development of online social networks has transformed social media from a way of communication between people into a tool for influencing people’s behaviour in their daily lives. This influence is often aimed at inciting protest movements in society and mobilising citizens for protest actions, and has a targeted impact on social network users. The sponsors and main actors of disruptive influences are often forces located in other countries. In the context of counteraction to targeted destructive influences, the task of identifying the network structure of destructive influence is very relevant. One element of this structure is the users connecting individual communities to the core of the protest network. These users are the bridges between the clusters and the core network. Their main task is to contribute to the rapid growth of the protest audience. Identifying the most influential bridges and blocking them could decrease the protest potential or make the protest actions ineffective. In this paper, we propose a methodology for identifying bridge users based on the original centrality measure of weighted contribution. Moreover, a method for identifying the most influential bridges is proposed. Unlike most probabilistic methods, weighted contribution centrality allows for clear determination of whether a user is a bridge or not. A description of the measure, a mathematical model and an algorithm for calculating it are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Castells, M.: Networks of Outrage and Hope Social Movements in the Internet Age. Polity, Cambridge (2012)

    Google Scholar 

  2. Gerbaudo, P.: Tweets and the Streets. Social Media and Contemporary Activism. Pluto Books, London (2012)

    Google Scholar 

  3. Faris, D.M.: Dissent and Revolution in a Digital Age. I.B.Tauris, London (2013). https://doi.org/10.5040/9780755607839

  4. Tindall, D.B.: From metaphors to mechanisms: critical issues in networks and social movements research. Soc. Netw. 29, 160–168 (2007). https://doi.org/10.1016/j.socnet.2006.07.001

    Article  Google Scholar 

  5. Bennett, W.L., Segerberg, A.: The logic of connective action. Inf. Commun. Soc. 15, 739–768 (2012). https://doi.org/10.1080/1369118X.2012.670661

    Article  Google Scholar 

  6. Juris, J.S.: Reflections on #Occupy everywhere: social media, public space, and emerging logics of aggregation. Am. Ethnol. 39, 259–279 (2012). https://doi.org/10.1111/j.1548-1425.2012.01362.x

    Article  Google Scholar 

  7. https://www.seuslab.ru/seus

  8. https://meduza.io/feature/2018/10/16/politsiya-po-vsey-rossii-pokupaet-sistemy-monitoringa-sotssetey-oni-pomogayut-iskat-ekstremizm-ne-vyhodya-iz-rabochego-kabineta

  9. Lü, L., Chen, D., Ren, X.-L., Zhang, Q.-M., Zhang, Y.-C., Zhou, T.: Vital nodes identification in complex networks. Phys. Rep. 650, 1–63 (2016). https://doi.org/10.1016/j.physrep.2016.06.007

    Article  MathSciNet  Google Scholar 

  10. Anthonisse, J.M.: The Rush in a Graph. Mathematisch Centrum (mimeo) (1971)

    Google Scholar 

  11. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35 (1977). https://doi.org/10.2307/3033543

  12. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1, 215–239 (1978). https://doi.org/10.1016/0378-8733(78)90021-7

    Article  Google Scholar 

  13. Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32, 245–251 (2010). https://doi.org/10.1016/j.socnet.2010.03.006

    Article  Google Scholar 

  14. Kuznetsov, E.N.: Analysis of the structure of network interactions: context-dependent measures of centrality. Management of large systems, pp. 57–82. IPU RAS, Moscow (2019)

    Google Scholar 

  15. Wang, H., Hernandez, J.M., van Mieghem, P.: Betweenness centrality in a weighted network. Phys. Rev. E 77, 046105 (2008). https://doi.org/10.1103/PhysRevE.77.046105

  16. van Mieghem, P., van Langen, S.: Influence of the link weight structure on the shortest path. Phys. Rev. E. 71, 056113 (2005). https://doi.org/10.1103/PhysRevE.71.056113

  17. Levandowsky, M., Winter, D.: Distance between sets. Nature 234, 34–35 (1971). https://doi.org/10.1038/234034a0

    Article  Google Scholar 

  18. Wei, H., et al.: Identifying influential nodes based on network representation learning in complex networks. PLOS ONE 13, e0200091 (2018). https://doi.org/10.1371/journal.pone.0200091

  19. Zhang, Q., Karsai, M., Vespignani, A.: Link transmission centrality in large-scale social networks. EPJ Data Sci. 7(1), 1–16 (2018). https://doi.org/10.1140/epjds/s13688-018-0162-8

    Article  Google Scholar 

  20. Ghalmane, Z., El Hassouni, M., Cherifi, C., Cherifi, H.: Centrality in modular networks. EPJ Data Sci. 8(1), 1–27 (2019). https://doi.org/10.1140/epjds/s13688-019-0195-7

    Article  Google Scholar 

  21. Jensen, P., et al.: Detecting global bridges in networks. IMA J. Complex Netw. 4, 319–329 (2015)

    Google Scholar 

  22. Alvarez-Socorro, A.J., Herrera-Almarza, G.C., González-Díaz, L.A.: Eigencentrality based on dissimilarity measures reveals central nodes in complex networks. Sci. Rep. 5, 17095 (2015). https://doi.org/10.1038/srep17095

  23. Rabchevskiy, A.N., Zayakin, V.S.: The program for calculating bridges in cluster networks. Certificate of state registration of computer programs No. 2021616086 of 16 April 2021 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrey N. Rabchevskiy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rabchevskiy, A.N., Zayakin, V.S., Rabchevskiy, E.A. (2022). A Method for Identifying Bridges in Online Social Networks. In: Burnaev, E., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2021. Communications in Computer and Information Science, vol 1573. Springer, Cham. https://doi.org/10.1007/978-3-031-15168-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15168-2_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15167-5

  • Online ISBN: 978-3-031-15168-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics