Maximizing rumor containment in social networks with constrained time

  • Lidan Fan
  • Weili Wu
  • Xuming Zhai
  • Kai Xing
  • Wonjun Lee
  • Ding-Zhu Du
Original Article

Abstract

The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee \(1-\frac{1}{e}\) for the two optimization problems.

Keywords

Rumor containment Influence diffusion Time deadline Personal interests Time delay 

References

  1. Alon N, Feldman M, Procaccia AD, Tennenholtz M (2010) A note on competitive diffusion through social networks. Inf Process Lett 110(6):221–225CrossRefMathSciNetMATHGoogle Scholar
  2. Bharathi S, Kempe D, Salek M (2007) Competitive influence maximization in social networks. In: Proceeding of the WINE, pp 306–311Google Scholar
  3. Borodin A, Filmus Y, Oren J (2010) Threshold models for competitive influence in social networks. In: Proceeding of the WINE, pp 539–550Google Scholar
  4. Budak C, Agrawal D, El Abbadi A (2011) Limiting the spread of misinformation in social networks. In: Proceeding of the WWWGoogle Scholar
  5. Carnes T, Nagarajan C, Wild SM, van Zuylen A (2007) Maximizing influence in a competitive social network: a follower’s perspective. In: ICEC ’07: Proceedings of the ninth international conference on electronic commerce, New York. ACM, New York, pp 351–360Google Scholar
  6. Chen W, Lu W, Zhang N (2012a) Time-critical influence maximization in social networks with time-delayed diffusion process (full technical report). CoRR abs/1204.3074Google Scholar
  7. Chen W, Lu W, Zhang N (2012b) Time-critical influence maximization in social networks with time-delayed diffusion process. In: Proceeding of the AAAI, pp 1–5Google Scholar
  8. Chen W, Wang C, Wang Y (2010a) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceeding of the KDD, pp 1029–1038Google Scholar
  9. Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceeding of the KDD, pp 199–208Google Scholar
  10. Chen W, Yuan Y, Zhang L (2010b) Scalable influence maximization in social networks under the linear threshold model. In: Proceeding of the ICDM, pp 88–97Google Scholar
  11. Fan L, Lu Z, Wu W, Thuraisingham B, Ma H, Bi Y (2013) Least cost rumor blocking in social networks. In: Proceeding of the ICDCSGoogle Scholar
  12. Goyal S, Kearns M (2012) Competitive contagion in networks. In: Proceeding of the 44th ACM symposium on theory of computing (STOC), pp 759–774Google Scholar
  13. He X, Song G, Chen W, Jiang Q (2012) Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceeding of the SDMGoogle Scholar
  14. Kempe D, Kleinberg JM, É Tardos (2003) Maximizing the spread of influence through a social network. In: Proceeding of the KDDGoogle Scholar
  15. Kostka J, Oswald YA, Wattenhofer R (2008) Word of mouth: rumor dissemination in social networks. In: Proceeding of the SIROCCO, pp 185–196Google Scholar
  16. Nemhauser G, Wolsey L, Fisher M (1978) An analysis of the approximations for maximizing submodular set functions. Math Program 14:265–294CrossRefMathSciNetMATHGoogle Scholar
  17. Nguyen NP, Yan G, Thai MT, Eidenbenz S (2012) Containment of misinformation spread in online social networks. In: Proceeding of the WebSciGoogle Scholar
  18. Nguyen TH, Tsai J, Jiang A, Bowring E, Maheswaran R, Tambe M (2012) Security games on social networks. In: Proceeding of the 2012 AAAI fall symposium seriesGoogle Scholar
  19. Pathak N, Banerjee A, Srivastava J (2010) A generalized linear threshold model for multiple cascades. In: Proceeding of the ICDM, pp 965–970Google Scholar
  20. Trpevski D, Tang WKS, Kocarev L (2010) Model for rumor spreading over networks. Phys Rev E 81:056102CrossRefGoogle Scholar
  21. Tsai J, Nguyen TH, Tambe M (2012) Security games for controlling contagion. In: Proceeding of the 26th national conference in articial intelligenceGoogle Scholar
  22. Tsai J, Qian Y, Vorobeychik Y, Kiekintveld C, Tambe M (2013) Bayesian security games for controlling contagion. In: Ito, Jonker, Gini, Shehory (eds) Proceedings of the 12th international conference on autonomous agents and multiagent systems (AAMAS 2013), May, Saint Paul, Minnesota, USA, pp 6–10Google Scholar
  23. Tzoumas V, Amanatidis C, Markakis E (2012) A game-theoretic analysis of a competitive diffusion process over social networks. In: Proceeding of the 8th international workshop on internet and network economics (WINE), vol 7695 of Lecture Notes in Computer Science. Springer, Berlin, pp 1–14Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Lidan Fan
    • 2
  • Weili Wu
    • 1
    • 3
  • Xuming Zhai
    • 1
  • Kai Xing
    • 1
  • Wonjun Lee
    • 4
  • Ding-Zhu Du
    • 1
  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardsonUSA
  2. 2.Department of Computer ScienceThe University of Texas at TylerTylerUSA
  3. 3.Taiyuan Institute of TechnologyTaiyuanPeople’s Republic of China
  4. 4.Department of Computer Science and EngineeringKorea UniversitySeoulRepublic of Korea

Personalised recommendations