Social Influence Maximization

  • Alireza RezvanianEmail author
  • Behnaz Moradabadi
  • Mina Ghavipour
  • Mohammad Mehdi Daliri Khomami
  • Mohammad Reza Meybodi
Part of the Studies in Computational Intelligence book series (SCI, volume 820)


Today, online social networks (OSNs) play an important role in diffusing information among people. Extensive research on social network analysis shows that online users trust to sharing information of acquaintances and they also may share that information. The repetitive process of information diffusion by a limited number of users can be very influential. In fact, with the rapid development and increasing popularity of online social networks, an enormous amount of information has been generated and diffused by human interactions through these networks. One of the fundamental problems in information diffusion in online social networks is the influence maximization problem. The problem of optimizing influence maximization in a social network focuses on how to select a small sub-set of individuals as the initial influence adopters to trigger a cascade such that the influence diffusion in the social network is maximized. In this chapter, two learning automata based approaches for influence maximization are introduced. The first one using discretized generalized confidence pursuit algorithm (DGCPA) tries to select proper seed set nodes and the second algorithm chooses heuristically seed set nodes using the results of minimum positive influence dominating set (MPIDS) with the aid of learning automata. Also the algorithm is investigated through both the mathematical analysis and simulation as well.


  1. Akbari Torkestani J, Meybodi MR (2011) A link stability-based multicast routing protocol for wireless mobile ad hoc networks. J Netw Comput Appl 34:1429–1440. Scholar
  2. Akbari Torkestani J, Meybodi MR (2012) Finding minimum weight connected dominating set in stochastic graph based on learning automata. Inf Sci (Ny) 200:57–77. Scholar
  3. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine BT—computer networks and ISDN systems. Comput Netw ISDN Syst 30:107–117. Scholar
  4. Daliri Khomami MM, Rezvanian A, Bagherpour N, Meybodi MR (2017) Irregular cellular automata based diffusion model for influence maximization. In: 2017 5th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, pp 69–74Google Scholar
  5. Daliri Khomami MM, Rezvanian A, Bagherpour N, Meybodi MR (2018) Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl Intell 48:570–593. Scholar
  6. Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’01. ACM, pp 57–66Google Scholar
  7. Ge H, Huang J, Di C et al (2017) Learning automata based approach for influence maximization problem on social networks. In: 2017 IEEE second international conference on data science in cyberspace (DSC). IEEE, pp 108–117Google Scholar
  8. Girvan M, Newman MEJ (2001) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826. Scholar
  9. Gleiser P, Danon L (2003) Community structure in jazz. Adv Complex Syst 6:565–573. Scholar
  10. Goyal A, Lu W, Lakshmanan LVS (2011) CELF++. In: Proceedings of the 20th international conference companion on World wide web—WWW ’11. ACM Press, New York, New York, USA, p 47Google Scholar
  11. Huang J, Ge H, Guo Y et al (2018) A learning automaton-based algorithm for influence maximization in social networks. pp 715–722Google Scholar
  12. Kanté MM, Limouzy V, Mary A, Nourine L (2011) Enumeration of minimal dominating sets and variants. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, pp 298–309Google Scholar
  13. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’03. p 137Google Scholar
  14. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46:604–632. Scholar
  15. Narendra KS, Thathachar MAL (1989) Learning automata: an introduction. Prentice-HallGoogle Scholar
  16. Lakshmivarahan S, Thathachar MAL (1976) Bounds on the convergence probabilities of learning automata. IEEE Trans Syst Man, Cybern A Syst Humans 6:756–763MathSciNetzbMATHGoogle Scholar
  17. Lee J-RR, Chung C-WW (2015) A query approach for influence maximization on specific users in social networks. IEEE Trans Knowl Data Eng 27:340–353. Scholar
  18. Leskovec J, Krause A, Guestrin C et al (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’07. ACM, p 420Google Scholar
  19. Liu B, Cong G, Zeng Y et al (2014) Influence spreading path and its application to the time constrained social influence maximization problem and beyond. IEEE Trans Knowl Data Eng 26:1904–1917. Scholar
  20. Lü L, Zhou T, Zhang QM, Stanley HE (2016) The H-index of a network node and its relation to degree and coreness. Nat Commun 7:10168. Scholar
  21. Lusseau D, Schneider K, Boisseau OJ et al (2003) The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54:396–405. Scholar
  22. Mashayekhi Y, Meybodi MR, Rezvanian A (2018) Weighted estimation of information diffusion probabilities for independent cascade model. In: 2018 4th international conference on web research (ICWR). IEEE, pp 63–69Google Scholar
  23. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining—KDD’02. p 61Google Scholar
  24. Wang G, Wang H, Tao X, Zhang J (2011) Positive influence dominating set in e-learning social networks. In: ICWL, pp 82–91Google Scholar
  25. Xu W, Lu Z, Wu W, Chen Z (2014) A novel approach to online social influence maximization. Soc Netw Anal Min 4:1–13. Scholar
  26. Yeruva S, Devi T, Reddy YS (2016) Selection of influential spreaders in complex networks using Pareto Shell decomposition. Phys A Stat Mech its Appl 452:133–144. Scholar
  27. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473. Scholar
  28. Zeng A, Zhang C-JJ (2013) Ranking spreaders by decomposing complex networks. Phys Lett Sect A Gen At Solid State Phys 377:1031–1035. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alireza Rezvanian
    • 1
    • 2
    Email author
  • Behnaz Moradabadi
    • 2
  • Mina Ghavipour
    • 2
  • Mohammad Mehdi Daliri Khomami
    • 2
  • Mohammad Reza Meybodi
    • 2
  1. 1.School of Computer ScienceInstitute for Research in Fundamental Sciences (IPM)TehranIran
  2. 2.Computer Engineering and Information Technology DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran

Personalised recommendations