Skip to main content
Springer Nature Link
Log in

How to Measure Influence in Social Networks?

  • Conference paper
  • First Online:
Research Challenges in Information Science (RCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

Included in the following conference series:

  • 2511 Accesses

Abstract

Today, social networks are a valued resource of social data that can be used to understand the interactions among people and communities. People can influence or be influenced by interactions, shared opinions and emotions. However, in the social network analysis, one of the main problems is to find the most influential people. This work aims to report on the results of literature review whose goal was to identify and analyse the metrics, algorithms and models used to measure the user influence on social networks. The search was carried out in three databases: Scopus, IEEEXplore, and ScienceDirect. We restricted published articles between the years 2014 until 2020, in English, and we used the following keywords: social networks analysis, influence, metrics, measurements, and algorithms. Backward process was applied to complement the search considering inclusion and exclusion criteria. As a result of this process, we obtained 25 articles: 12 in the initial search and 13 in the backward process. The literature review resulted in the collection of 21 influence metrics, 4 influence algorithms, and 8 models of influence analysis. We start by defining influence and presenting its properties and applications. We then proceed by describing, analysing and categorizing all that were found metrics, algorithms, and models to measure influence in social networks. Finally, we present a discussion on these metrics, algorithms, and models. This work helps researchers to quickly gain a broad perspective on metrics, algorithms, and models for influence in social networks and their relative potentialities and limitations.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Newman, M., Watts, D.J., Barabási, A.-L.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006)

    MATH  Google Scholar 

  2. Wright, A.: Glut: Mastering Information Through the Ages. Cornell University Press, Ithaca (2008)

    Google Scholar 

  3. Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81(2), 591–646 (2009)

    Article  Google Scholar 

  4. Hidalgo, C.A.: Disconnected, fragmented, or united? A trans-disciplinary review of network science. Appl. Netw. Sci. 1(1), 6 (2016)

    Article  Google Scholar 

  5. Wurman, R.S.: Information Anxiety 2, 2nd edn. QUE (2001)

    Google Scholar 

  6. Hansen, D., Shneiderman, B., Smith, M.: Analyzing Social Media Networks with NodeXL: Insights from a Connected World, 1st edn. Morgan Kaufmann (2010)

    Google Scholar 

  7. Peng, S., Wang, G., Xie, D.: Social influence analysis in social networking big data: opportunities and challenges. IEEE Netw. 31(1), 11–17 (2017)

    Article  Google Scholar 

  8. Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., Jia, W.: Influence analysis in social networks: a survey. J. Netw. Comput. Appl. 106(January), 17–32 (2018)

    Article  Google Scholar 

  9. Yu, S., Liu, M., Dou, W., Liu, X., Zhou, S.: Networking for big data: a survey. IEEE Commun. Surv. Tutor. 19(1), 531–549 (2017)

    Article  Google Scholar 

  10. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 11, pp. 137–146 (2003)

    Google Scholar 

  11. Azevedo, B.M., Oliveira e Sá, J., Baptista, A.A., Branco, P.: Information visualization: conceptualizing new paths for filtering and navigate in scientific knowledge objects. In: 2017 24o Encontro Português de Computação Gráfica e Interação (EPCGI), pp. 1–8 (2017)

    Google Scholar 

  12. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a review. MIS Q. 26(2), 13 (2002)

    Google Scholar 

  13. Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering - a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009)

    Article  Google Scholar 

  14. Mongeon, P., Paul-Hus, A.: The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106(1), 213–228 (2016)

    Article  Google Scholar 

  15. Li, K., Zhang, L., Huang, H.: Social influence analysis: models, methods, and evaluation. Engineering 4(1), 40–46 (2018)

    Article  Google Scholar 

  16. Almgren, K., Lee, J.: An empirical comparison of influence measurements for social network analysis. Soc. Netw. Anal. Min. 6(52), 1–18 (2016)

    Google Scholar 

  17. Kumar, N., Guo, R., Aleali, A., Shakarian, P.: An empirical evaluation of social influence metrics (2016)

    Google Scholar 

  18. Merriam, W.: Definition of influence. In: Definition of influence (2011)

    Google Scholar 

  19. Li, H., Cui, J.-T., Ma, J.-F.: Social influence study in online networks: a three-level review. J. Comput. Sci. Technol. 30(1), 184–199 (2015)

    Article  MathSciNet  Google Scholar 

  20. More, J.S., Lingam, C.: A gradient-based methodology for optimizing time for influence diffusion in social networks. Soc. Netw. Anal. Min. 9(1), 5 (2019)

    Article  Google Scholar 

  21. Jalayer, M., Azheian, M., Agha Mohammad Ali Kermani, M.: A hybrid algorithm based on community detection and multi attribute decision making for influence maximization. Comput. Ind. Eng. 120, 234–250 (2018)

    Article  Google Scholar 

  22. Liqing, Q., Jinfeng, Y., Xin, F., Wei, J., Wenwen, G.: Analysis of Influence Maximization in large-Scale Social Networks. IEEE Access 7(4), 42052–42062 (2019)

    Article  Google Scholar 

  23. Li, D., Shuai, X., Sun, G., Tang, J., Ding, Y., Luo, Z.: Mining topic-level opinion influence in microblog. In: ACM International Conference Proceeding Series, pp. 1562–1566 (2012)

    Google Scholar 

  24. Li, N., Gillet, D.: Identifying influential scholars in academic social media platforms. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 608–614 (2013)

    Google Scholar 

  25. Kong, X., Shi, Y., Yu, S., Liu, J., Xia, F.: Academic social networks: modeling, analysis, mining and applications. J. Netw. Comput. Appl. 132, 86–103 (2019)

    Article  Google Scholar 

  26. Kaur, M., Singh, S.: Analyzing negative ties in social networks: a survey. Egypt. Inform. J. 17(1), 21–43 (2016)

    Article  Google Scholar 

  27. Riquelme, F., González-Cantergiani, P.: Measuring user influence on Twitter: a survey. Inf. Process. Manag. 52(5), 949–975 (2016)

    Article  Google Scholar 

  28. Shelke, S., Attar, V.: Source detection of rumor in social network – a review. Online Soc. Netw. Media 9, 30–42 (2019)

    Article  Google Scholar 

  29. Peng, S., Yu, S., Yang, A.: Smartphone malware and its propagation modeling: a survey. IEEE Commun. Surv. Tutor. 16(2), 925–941 (2014)

    Article  Google Scholar 

  30. Russell Bernard, H.: The development of social network analysis: a study in the sociology of science. Soc. Netw. 27(4), 377–384 (2005)

    Article  Google Scholar 

  31. Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)

    Article  MathSciNet  Google Scholar 

  32. Frantz, T.L., Cataldo, M., Carley, K.M.: Robustness of centrality measures under uncertainty: examining the role of network topology. Comput. Math. Organ. Theory 15(4), 303–328 (2009)

    Article  Google Scholar 

  33. Kosorukoff, A.: Theory. In: Social Network Analysis - Theory and Applications, pp. 1–4 (2011)

    Google Scholar 

  34. Liao, H., Mariani, M.S., Medo, M., Zhang, Y.C., Zhou, M.Y.: Ranking in evolving complex networks. Phys. Rep. 689, 1–54 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  35. Brin, S., Page, L.: Reprint of: the anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)

    Article  Google Scholar 

  36. Liu, Q., et al.: An influence propagation view of PageRank. ACM Trans. Knowl. Discov. Data 11(3), 2–28 (2017)

    Google Scholar 

  37. Wang, Y., Feng, X.: A potential-based node selection strategy for influence maximization in a social network. In: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (ADMA 2009), pp. 350–361 (2009)

    Google Scholar 

  38. Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks categories and subject descriptors. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048 (2010)

    Google Scholar 

  39. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Vanbriesen, J.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)

    Google Scholar 

  40. Zhou, T., Cao, J., Liu, B., Xu, S., Zhu, Z., Luo, J.: Location-based influence maximization in social networks. In: CIKM 2015, no. 93, pp. 1211–1220 (2015)

    Google Scholar 

  41. Chorley, M.J., Colombo, G.B., Allen, S.M., Whitaker, R.M.: Human content filtering in Twitter: the influence of metadata. Int. J. Hum Comput. Stud. 74, 32–40 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This work has been supported by IViSSEM: POCI-01-0145-FEDER-28284, COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

Author information

Authors and Affiliations

  1. Centro ALGORITMI, University of Minho, 4800-058, Guimarães, Portugal

    Ana Carolina Ribeiro, Bruno Azevedo, Jorge Oliveira e Sá & Ana Alice Baptista

Authors
  1. Ana Carolina Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruno Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Oliveira e Sá
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ana Alice Baptista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Carolina Ribeiro .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ribeiro, A.C., Azevedo, B., Oliveira e Sá, J., Baptista, A.A. (2020). How to Measure Influence in Social Networks?. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50316-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics