Skip to main content
SpringerLink
Log in

Understanding information interactions in diffusion: an evolutionary game-theoretic perspective

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Social networks are fundamental mediums for diffusion of information and contagions appear at some node of the network and get propagated over the edges. Prior researches mainly focus on each contagion spreading independently, regardless of multiple contagions’ interactions as they propagate at the same time. In the real world, simultaneous news and events usually have to compete for user’s attention to get propagated. In some other cases, they can cooperate with each other and achieve more influences.

In this paper, an evolutionary game theoretic framework is proposed to model the interactions among multiple contagions. The basic idea is that different contagions in social networks are similar to the multiple organisms in a population, and the diffusion process is as organisms interact and then evolve from one state to another. This framework statistically learns the payoffs as contagions interacting with each other and builds the payoff matrix. Since learning payoffs for all pairs of contagions IS almost impossible (quadratic in the number of contagions), a contagion clustering method is proposed in order to decrease the number of parameters to fit, which makes our approach efficient and scalable. To verify the proposed framework, we conduct experiments by using real-world information spreading dataset of Digg. Experimental results show that the proposed game theoretic framework helps to comprehend the information diffusion process better and can predict users’ forwarding behaviors with more accuracy than the previous studies. The analyses of evolution dynamics of contagions and evolutionarily stable strategy reveal whether a contagion can be promoted or suppressed by others in the diffusion process.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Physical Review Letters, 2001, 86(14): 3200–3203

    Article  Google Scholar 

  2. Galuba W, Aberer K, Chakraborty D, Despotovic Z, Kellerer W. Outtweeting the twitterers-predicting information cascades in microblogs. In: Proceedings of the 3rd Conference on Online Social Networks. 2010, 3–11

    Google Scholar 

  3. Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M. Patterns cascading behavior in large blog graphs. In: Proceedings of the 7th SIAM International Conference on Data Mining. 2007, 551–556

    Google Scholar 

  4. Yang J, Leskovec J. Modeling information diffusion in implicit networks. In: Proceedings of the 10th International Conference on Data Mining. 2010, 599–608

    Google Scholar 

  5. Centola D, Macy M. Complex contagions and the weakness of long ties. American Journal of Sociology, 2007, 113(3): 702–734

    Article  Google Scholar 

  6. Cosley D, Huttenlocher D, Kleinberg J M, Lan X, Suri S. Sequential influence models in social networks. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010, 26–33

    Google Scholar 

  7. Wu S, Hofman J M, Mason W A, Watts D J. Who says what to whom on twitter. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 705–714

    Google Scholar 

  8. Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2003, 137–146

    Google Scholar 

  9. Kempe D, Kleinberg J, Tardos É. Influential nodes in a diffusion model for social networks. Automata, Languages and Programming, 2005, 1127–1138

    Chapter  Google Scholar 

  10. Weng L, Flammini A, Vespignani A, Menczer F. Competition among memes in a world with limited attention. Scientific Reports, 2012, 2

    Google Scholar 

  11. Myers S A, Leskovec J. Clash of the contagions: cooperation and competition in information diffusion. In: Proceedings of the 12th International Conference on Data Mining. 2012, 539–548

    Google Scholar 

  12. Smith J M, Price G R. The logic of animal conflict. Nature, 1973, 246: 15–18

    Article  Google Scholar 

  13. McNamara J M, Gasson C E, Houston A I. Incorporating rules for responding into evolutionary games. Nature, 1999, 401(6751): 368–371

    Google Scholar 

  14. May R M, Leonard W J. Nonlinear aspects of competition between three species. SIAM Journal on Applied Mathematics, 1975, 29(2): 243–253

    Article  MathSciNet  MATH  Google Scholar 

  15. Doebeli M, Knowlton N. The evolution of interspecific mutualisms. In: Proceedings of the National Academy of Sciences, 1998, 95(15): 8676–8680

    Google Scholar 

  16. Axelrod R, Hamilton W D. The evolution of cooperation. Science, 1981, 211(4489): 1390–1396

    Article  MathSciNet  MATH  Google Scholar 

  17. Nowak M A, Sigmund K. Evolution of indirect reciprocity. Nature, 2005, 437(7063): 1291–1298

    Article  Google Scholar 

  18. Nowak M A, Komarova N L, Niyogi P. Computational and evolutionary aspects of language. Nature, 2002, 417(6889): 611–617

    Article  Google Scholar 

  19. Easley D, Kleinberg J. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, 2010

    Book  MATH  Google Scholar 

  20. Weibull J W. Evolutionary game theory. MIT Press, 1997

    MATH  Google Scholar 

  21. Gomez-Rodriguez M, Leskovec J, Krause A. Inferring networks of diffusion and influence. ACM Transactions on Knowledge Discovery from Data, 2012, 5(4): 21

    Article  Google Scholar 

  22. Sadikov E, Medina M, Leskovec J, Garcia-Molina H. Correcting for missing data in information cascades. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 55–64

    Google Scholar 

  23. Zheng X, Zhong Y, Zeng D, Wang F Y. Social influence and spread dynamics in social networks. Frontiers of Computer Science, 2012, 6(5): 611–620

    MathSciNet  Google Scholar 

  24. Bakshy E, Hofman J M, Mason W A, Watts D J. Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 65–74

    Google Scholar 

  25. Sneppen K, Trusina A, Jensen M H, Bornholdt S. A minimal model for multiple epidemics and immunity spreading. PloS One, 2010, 5(10): e13326

    Article  Google Scholar 

  26. Kuhlman C J, Kumar V S A, Marathe M V, Swarup S, Tuli G, Ravi S S, Rosenkrantz D J. Inhibiting the diffusion of contagions in bi-threshold systems: analytical and experimental results. AAAI Fall Symposium: Complex Adaptive Systems, 2011

    Google Scholar 

  27. Wang F, Wang H Y, Xu K. Diffusive logistic model towards predicting information diffusion in online social networks. In: Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops. 2012, 133–139

    Google Scholar 

  28. Granovetter M. Threshold models of collective behavior. American Journal of Sociology, 1978: 1420–1443

    Google Scholar 

  29. Schelling T. Micromotives and macromotives. Norton, 1978

    Google Scholar 

  30. Goldenberg J, Libai B, Muller E. Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters, 2001, 12(3): 211–223

    Article  Google Scholar 

  31. Hethcote H W. The mathematics of infectious diseases. SIAM Review, 2000, 42(4): 599–653

    Article  MathSciNet  MATH  Google Scholar 

  32. Newman M E. The structure and function of complex networks. SIAM Review, 2003, 45(2): 167–256

    Article  MathSciNet  MATH  Google Scholar 

  33. Pathak N, Banerjee A, Srivastava J. A generalized linear threshold model for multiple cascades. In: Proceedings of the 10th International Conference on Data Mining. 2010, 965–970

    Google Scholar 

  34. Prakash B, Beutel A, Rosenfeld R, Faloutsos C. Winner takes all: competing viruses or ideas on fair-play networks. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 1037–1046

    Google Scholar 

  35. Karrer B, Newman M E J. Competing epidemics on complex networks. Physical Review E, 2011, 84(3): 036106

    Google Scholar 

  36. Bharathi S, Kempe D, Salek M. Competitive influence maximization in social networks. Internet and Network Economics, 2007: 306–311

    Chapter  Google Scholar 

  37. Budak C, Agrawal D, EL Abbadi A. Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 665–674

    Google Scholar 

  38. Narayanam R, Narahari Y. A shapley value-based approach to discover influential nodes in social networks. IEEE Transactions on Automation Science and Engineering, 2010, 99: 1–18

    Google Scholar 

  39. Chen W, Liu Z M, Sun X R, Wang Y J. A game-theoretic framework to identify overlapping communities in social networks. Data Mining and Knowledge Discovery, 2010, 21(2): 224–240

    Article  MathSciNet  Google Scholar 

  40. Jiang C X, Chen Y, Liu K J R. Evolutionary information diffusion over social networks. 2013, arXiv preprint arXiv:1309.2920

    Google Scholar 

  41. Ferrara E, JafariAsbagh M, Varol O, Qazvinian V, Menczer F, Flammini A. Clustering memes in social media. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2013, 548–555

    Google Scholar 

  42. Hogg T, Lerman K. Social dynamics of digg. EPJ Data Science, 2012, 1(1): 1–26

    Article  Google Scholar 

  43. Lerman K, Ghosh R. Information contagion: an empirical study of the spread of news on digg and twitter social networks. ICWSM, 2010, 10: 90–97

    Google Scholar 

  44. Davis J, Goadrich M. The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 233–240

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yuan Su, Xi Zhang, Lixin Liu, Shouyou Song & Binxing Fang

Authors
  1. Yuan Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lixin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shouyou Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Binxing Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xi Zhang.

Additional information

Yuan Su received his Master degree in computer science and technology from Beijing University of Posts and Telecommunications (BUPT), China in 2012. He is currently a PhD candidate there. His research interest is social network analysis and data mining. He is currently working on modeling and predicting the information diffusion in social networks.

Xi Zhang received his BS degree from Harbin Institute of Technology, China in 2006, and his PhD degree from Tsinghua University, China in 2012. He is now an assistant professor in School of Computer Science at Beijing University of Posts and Telecommunications (BUPT), China. He is also in Key Laboratory of Trustworthy Distributed Computing and Service ofMinistry of Education, China. He has worked in the areas of data mining and computer architecture.

Lixin Liu received his BS degree in software engineering from Hebei University of Technology, China in 2012 and is currently a master student in Beijing University of Posts and Telecommunications (BUPT), China. His research interest is data mining. He is currently working on large scale graph mining.

Shouyou Song received his BS degree in computer science and technology from Huaqiao University, China in 2012 and is currently a master student in Beijing University of Posts and Telecommunications (BUPT), China. His research interest is complex networks and data mining. He is currently working on community detection in social networks.

Binxing Fang is a member of the Chinese Academy of Engineering and a professor in School of Computer Science at Beijing University of Posts and Telecommunications (BUPT), China. He received his PhD from Harbin Institute of Technology, China in 1989. He is currently the chief scientist of State Key Development Program of Basic Research of China, with the name Basic Research of Social Network Analysis and Network Information Diffusion. His current interests include social network analysis, IOT search, and information security.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Su, Y., Zhang, X., Liu, L. et al. Understanding information interactions in diffusion: an evolutionary game-theoretic perspective. Front. Comput. Sci. 10, 518–531 (2016). https://doi.org/10.1007/s11704-015-5008-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-015-5008-y

Keywords

Search

Navigation