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Influential Nodes Detection in Dynamic Social Networks

  • Nesrine HafieneEmail author
  • Wafa Karoui
  • Lotfi Ben Romdhane
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 354)

Abstract

The influence maximization problem aims to identify influential nodes allowing to reach the viral marketing objectives on social networks. Previous researches are mainly concerned with the static social network analysis and the development of algorithms in this context. However, when network changes, those algorithms must be updated. In this paper, we offer a new interesting approach to study the influential nodes detection problem in changing social networks. This approach can be considered to be an extension of a previous static algorithm SND (Semantic and structural influential Nodes Detection). Experimental results prove the effectiveness of SNDUpdate to detect influential nodes in dynamic social networks.

Keywords

Dynamic social networks Influence maximization Influence propagation Influential nodes 

References

  1. 1.
    Kitsak, M., Gallos, L., Havlin, S.: Identification of influential spreaders in complex networks. Nature Phys. 6, 888–893 (2010)CrossRefGoogle Scholar
  2. 2.
    Hafiene, N., Karoui, W.: A new structural and semantic approach for identifying influential nodes in social networks. In: IEEE/ACS International Conference of Computer Systems and Applications AICCSA, pp. 1338–1345 (2017)Google Scholar
  3. 3.
    Chen, X., Song, G., He, X., Xie, K.: On influential nodes tracking in dynamic social networks. IEEE Trans. Knowl. Data Eng. 29, 359–372 (2015)Google Scholar
  4. 4.
    Liu, X., et al.: On the shoulders of giants: incremental influence maximization in evolving social networks. Complexity 1–14 (2017)Google Scholar
  5. 5.
    Wang, T., Dai, W., Jiao, P., Wang, W.: Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model. Int. J. Mod. Phys. B 30(16), 1–18 (2016)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Basaras, P., Katsaros, D., Tassiulas, L.: Detecting influential spreaders in complex, dynamic networks. Computer 46, 24–29 (2013)CrossRefGoogle Scholar
  7. 7.
    Aggarwal, C.C., Lin, S., Yu, P.S.: On influential node discovery in dynamic social networks. In: International Conference on Data Mining, pp. 636–647 (2012)Google Scholar
  8. 8.
    Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. In: International Conference on Data Mining, pp. 636–647 (2012)Google Scholar
  9. 9.
    Yang, Y., Wang, Z., Pei, J., Chen, E.: Tracking influential nodes in dynamic networks. IEEE Trans. Knowl. Data Eng. 29, 2615–2628 (2017)CrossRefGoogle Scholar
  10. 10.
    Yang, Y., Wang, Z., Jin, T., Pei, J., Chen, E.: Tracking top-k influential vertices in dynamic networks. IEEE Trans. Knowl. Data Eng. 29, 1–14 (2018)Google Scholar
  11. 11.
    Sobolevsky, S., Ratti, C., Campari, R.: General optimization technique for high-quality community detection in complex networks. Phys. Rev. 90, 1–19 (2014)Google Scholar
  12. 12.
    Feng, S., Wang, L., Sun, S., Xia, C.: Synchronization properties of interconnected network based on the vital node. Non Linear Dyn. 93(2), 335–347 (2018)CrossRefGoogle Scholar
  13. 13.
    Tong, G., Weili, W., Tang, S., Du, D.-Z.: Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans. Netw. 25(1), 112–125 (2017)CrossRefGoogle Scholar
  14. 14.
    Ren, J., Wang, C., Liu, Q., Wang, G., Dong, J.: Identify influential spreaders in complex networks based on potential edge weights. Int. J. Innov. Comput. Inf. Control 12(2), 581–590 (2016)Google Scholar
  15. 15.
    Wei, W., Carley, K.: Measuring temporal patterns in dynamic social networks. J. ACM Trans. Knowl. Discov. Data 10(1), 1–27 (2015)CrossRefGoogle Scholar
  16. 16.
    Ohsaka, N., Akiba, T., Yoshida, Y., Kawarabayashi, K.: Dynamic influence analysis in evolving networks. J. Proc. VLDB Endow. VLDB 9(12), 1077–1088 (2016)CrossRefGoogle Scholar
  17. 17.
    Zeng, A., Zhang, C.-J.: Ranking spreaders by decomposing complex networks. Phys. Lett. 377, 1031–1035 (2013)CrossRefGoogle Scholar
  18. 18.
    Zhuang, H., Sun, Y., Tang, J., Zhang, J., Sun, X.: Influence maximization in dynamic social networks. In: International Conference on Data Mining, pp. 636–647 (2013)Google Scholar
  19. 19.
    Wang, Y., Zhu, J., Ming, Q.: Incremental influence maximization for dynamic social networks. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds.) ICPCSEE 2017. CCIS, vol. 728, pp. 13–27. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-6388-6_2CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nesrine Hafiene
    • 1
    • 2
    Email author
  • Wafa Karoui
    • 1
    • 3
  • Lotfi Ben Romdhane
    • 1
    • 2
  1. 1.Laboratoire MARS LR17ES05, ISITComUniversité de SousseSousseTunisie
  2. 2.ISITComUniversité de SousseSousseTunisie
  3. 3.ISIUniversité de Tunis El ManarTunisTunisie

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