Skip to main content
Springer Nature Link
Log in

Data-Driven Influential Nodes Identification in Dynamic Social Networks

  • Conference paper
  • First Online:
Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2021)

Abstract

The identification of influential nodes in social networks has significant commercial and academic value in advertising, information management, and user behavior analysis. Previous work only studies the simple topology of the network without considering the dynamic propagation characteristics of the network, which does not fit the actual scene and hinders wide application. To solve the problem, We develop a data-driven model for the identification of influential nodes in dynamic social networks. Firstly, we introduce an influence evaluation metric BTRank based on user interaction behavior and topic relevance of the information. Combining BTRank, LH-index, and betweenness centrality, we construct a multi-scale comprehensive metric system. Secondly, in order to optimize the metric weights calculated by entropy weight method, we use simulation data to train a regression model and obtain the metric weights by Gradient Descent Algorithm. Thirdly, the weights obtained from training are used in weighted TOPSIS to sort the influence of nodes and identify influential nodes among them. Finally, We compare our model with existing models on four real-world networks. The experimental results have demonstrated significant improvement in both accuracy and effectiveness achieved by our proposed model.

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. Hafiene, N., Karoui, W., Romdhane, L.B.: Influential nodes detection in dynamic social networks: a survey. Expert Syst. Appl. 159, 113642 (2020)

    Article  Google Scholar 

  2. Bian, T., Deng, Y.: A new evidential methodology of identifying influential nodes in complex networks. Chaos Solitons Fractals 103, 101–110 (2017)

    Article  MathSciNet  Google Scholar 

  3. Liern, V., Pérez-Gladish, B.: Multiple criteria ranking method based on functional proximity index: un-weighted TOPSIS. Ann. Oper. Res. 1–23 (2020). https://doi.org/10.1007/s10479-020-03718-1

  4. Lu, M.: Node importance evaluation based on neighborhood structure hole and improved TOPSIS. Comput. Networks 178, 107336 (2020)

    Article  Google Scholar 

  5. Martin, T., Zhang, X., Newman, M.E.: Localization and centrality in networks. Phys. Rev. E 90(5), 052808 (2014)

    Article  Google Scholar 

  6. Hu, R.J., Li, Q., Zhang, G.Y., Ma, W.C.: Centrality measures in directed fuzzy social networks. Fuzzy Inf. Eng. 7(1), 115–128 (2015)

    Article  MathSciNet  Google Scholar 

  7. Singh, R.R., Goel, K., Iyengar, S., Gupta, S.: A faster algorithm to update betweenness centrality after node alteration. Internet Math. 11(4–5), 403–420 (2015)

    Article  MathSciNet  Google Scholar 

  8. Wang, Z., Zhao, Y., Xi, J., Du, C.: Fast ranking influential nodes in complex networks using a k-shell iteration factor. Phys. A Stat. Mech. Appl. 461, 171–181 (2016)

    Article  Google Scholar 

  9. Lee, J.K., Choi, J., Kim, C., Kim, Y.: Social media, network heterogeneity, and opinion polarization. J. Commun. 64(4), 702–722 (2014)

    Article  Google Scholar 

  10. Yu, D., Chen, N., Ran, X.: Computational modeling of Weibo user influence based on information interactive network. Online Inf. Rev. (2016)

    Google Scholar 

  11. Sun, X., Xie, F.: The three-degree calculation model of microblog users’ influence (short paper). In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds.) CollaborateCom 2018. LNICST, vol. 268, pp. 151–160. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12981-1_10

    Chapter  Google Scholar 

  12. Ren, T., et al.: Identifying vital nodes based on reverse greedy method. Sci. Rep. 10(1), 1–8 (2020)

    Article  Google Scholar 

  13. Zheng, Z., Gao, X., Ma, X., Chen, G.: Predicting hot events in the early period through Bayesian model for social networks. IEEE Trans. Knowl. Data Eng. (2020)

    Google Scholar 

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

    Article  Google Scholar 

  15. Drakopoulos, G., Kanavos, A., Tsakalidis, A.K.: Evaluating twitter influence ranking with system theory. In: WEBIST (1), pp. 113–120 (2016)

    Google Scholar 

  16. Bo, H., McConville, R., Hong, J., Liu, W.: Social network influence ranking via embedding network interactions for user recommendation. In: Companion Proceedings of the Web Conference 2020, pp. 379–384 (2020)

    Google Scholar 

  17. Sapul, M.S.C., Aung, T.H., Jiamthapthaksin, R.: Trending topic discovery of twitter tweets using clustering and topic modeling algorithms. In: 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6. IEEE (2017)

    Google Scholar 

  18. Liu, Q., et al.: Leveraging local h-index to identify and rank influential spreaders in networks. Phys. A Stat. Mech. Appl. 512, 379–391 (2018)

    Article  Google Scholar 

  19. Chen, P.: Effects of the entropy weight on TOPSIS. Expert Syst. Appl. 168, 114186 (2021)

    Article  Google Scholar 

  20. Ning, Z., Iradukunda, H.N., Zhang, Q., Zhu, T.: Benchmarking machine learning: how fast can your algorithms go? arXiv preprint arXiv:2101.03219 (2021)

  21. Yuan, B., Chang, J.E., Zhang, F.: Influential node identification method of assembly system based on TOPSIS and topology. J. Phys. Conf. Ser. 1605, 012019 (2020)

    Article  Google Scholar 

  22. Li, P., Liu, K., Li, K., Liu, J., Zhou, D.: Estimating user influence ranking in independent cascade model. Phys. A Stat. Mech. Appl. 565, 125584 (2021)

    Article  MathSciNet  Google Scholar 

  23. Batyrshin, I.Z., Ramirez-Mejia, I., Batyrshin, I.I., Solovyev, V.: Similarity-Based correlation functions for binary data. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds.) MICAI 2020. LNCS (LNAI), vol. 12469, pp. 224–233. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60887-3_20

    Chapter  Google Scholar 

  24. Guo, C., Yang, L., Chen, X., Chen, D., Gao, H., Ma, J.: Influential nodes identification in complex networks via information entropy. Entropy 22(2), 242 (2020)

    Article  MathSciNet  Google Scholar 

  25. Sun, H., Chen, D., He, J., Ch’ng, E.: A voting approach to uncover multiple influential spreaders on weighted networks. Phys. A Stat. Mech. Appl. 519, 303–312 (2019)

    Article  Google Scholar 

  26. Basu, S., Maulik, U.: Mining important nodes in complex networks using nonlinear PCA. In: 2017 IEEE Calcutta Conference (CALCON), pp. 469–473. IEEE (2017)

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Key Research and Development Plan in China (2018YFC0830500), National Natural Science Foundation of China (62172278).

Author information

Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ye Qian & Li Pan

  2. National Engineering Laboratory for Information Content Analysis Technology, Shanghai, China

    Li Pan

Authors
  1. Ye Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Pan .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qian, Y., Pan, L. (2021). Data-Driven Influential Nodes Identification in Dynamic Social Networks. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92635-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92634-2

  • Online ISBN: 978-3-030-92635-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics