Soft Computing

, Volume 21, Issue 18, pp 5281–5293 | Cite as

Modeling and minimizing information distortion in information diffusion through a social network

Foundations

Abstract

It is very common in real life that information distorts during the process of transmission in a social network, which may lead to people’s incorrect comprehension of the information and further poor decision making. In this paper, we study how to model and minimize the distortion of information when it diffuses through a social network. We propose the concept of information authenticity to measure distortion as well as a mathematical model to characterize how information distorts during its diffusion through a social network, and study the optimization problem of maximizing the information authenticity of a social network. In order to solve the problem, we employ a framework of greedy algorithms that was proposed by Ni et al. (Inf Sci 180(13):2514–2527, 2010), which can trade off between optimality and complexity. Finally, we perform experiments to show the greedy algorithms can effectively solve the problem we propose.

Keywords

Social networks Information distortion Information authenticity Stochastic simulation Greedy algorithm 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China (Nos. 71471038, 71101027, 71001080, 71171191) and Beijing Higher Education Young Elite Teacher Project (No. YETP0909).

Compliance with ethical standards

Conflict of interest

Yaodong Ni, Liu Ning, Hua Ke, Xiaoyu Ji declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47MathSciNetCrossRefMATHGoogle Scholar
  2. Albert R, Jeong H, Barabási A-L (1999) Internet: diameter of the world-wide web. Nature 401(6749):130–131CrossRefGoogle Scholar
  3. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefMATHGoogle Scholar
  4. Berger N, Borgs C, Chayes JT, Saberi A (2005) On the spread of viruses on the internet. In: Proceedings of the sixteenth annual ACM-SIAM symposium on discrete algorithms. Society for Industrial and Applied Mathematics, pp 301–310Google Scholar
  5. Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14:350–362CrossRefGoogle Scholar
  6. Carley KM, Lin Z (1997) A theoretical study of organizational performance under information distortion. Manag Sci 43(7):976–997CrossRefMATHGoogle Scholar
  7. Chen N (2009) On the approximability of influence in social networks. SIAM J Discrete Math 23(3):1400–1415MathSciNetCrossRefMATHGoogle Scholar
  8. Deffuant G, Huet S, Amblard F (2005) An individual-based model of innovation diffusion mixing social value and individual benefit. Am J Sociol 110(4):1041–1069CrossRefGoogle Scholar
  9. DeKay ML, Patiño-Echeverri D, Fischbeck PS (2009) Distortion of probability and outcome information in risky decisions. Organ Behav Hum Decis Process 109(1):79–92CrossRefGoogle Scholar
  10. Dezső Z, Barabási A-L (2002) Halting viruses in scale-free networks. Phys Rev E 65(5):055103CrossRefGoogle Scholar
  11. 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. ACM, pp 57–66Google Scholar
  12. Ebbinghaus H (1886) A supposed law of memory. Mind 42:300-aGoogle Scholar
  13. Erdös P, Rényi A (1959) On random graphs I. Publ. Math. 6:290–297MathSciNetMATHGoogle Scholar
  14. Ganesh A, Massoulié L, Towsley D (2005) The effect of network topology on the spread of epidemics. In 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE INFOCOM (2005), vol 2. IEEE, pp 1455–1466Google Scholar
  15. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223CrossRefGoogle Scholar
  16. Grabowski A, Kruszewska N, Kosiński RA (2008) Properties of on-line social systems. Eur Phys J B 66(1):107–113CrossRefMATHGoogle Scholar
  17. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443CrossRefGoogle Scholar
  18. Hong R, Pan J, Hao S, Wang M, Xue F, Wu X (2014) Image quality assessment based on matching pursuit. Inf Sci 273:196–211Google Scholar
  19. Jackson MO (2010) Social and economic networks. Princeton University Press, PrincetonMATHGoogle Scholar
  20. Jing L, Zhao J, Cao F (2014) Extended feed forward neural networks with random weights for face recognition. Neurocomputing 136:96–102CrossRefGoogle Scholar
  21. 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. ACM, pp 137–146Google Scholar
  22. Kempe D, Kleinberg J, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: Proceedings of the thirty-second international colloquium on automata, languages and programming. Springer, pp 1127–1138Google Scholar
  23. Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558CrossRefMATHGoogle Scholar
  24. Leskovec J (2007) Stanford large network dataset collection. http://snap.stanford.edu/data/
  25. Mishra BK, Raghunathan S, Yue X (2007) Information sharing in supply chains: incentives for information distortion. IIE Trans 39(9):863–877CrossRefGoogle Scholar
  26. Monclar R, Tecla A, Oliveira J, de Souza JM (2009) Mek: using spatial-temporal information to improve social networks and knowledge dissemination. Inf Sci 179(15):2524–2537CrossRefGoogle Scholar
  27. Morris S (2000) Contagion. Rev Econ Stud 67(1):57–78MathSciNetCrossRefMATHGoogle Scholar
  28. Mossel E, Roch S (2007) On the submodularity of influence in social networks. In: Proceedings of the thirty-ninth annual ACM symposium on theory of computing. ACM, pp 128–134Google Scholar
  29. Ni Y, Liu Z-Q (2011) Heuristic search for optimizing diffusion of influence in a social network under the resource constraint. Soft Comput 15(2):335–344CrossRefGoogle Scholar
  30. Ni Y, Shi Q (2013) Minimizing the complete influence time in a social network with stochastic costs for influencing nodes. Int J Uncertain Fuzziness Knowl Based Syst 21(S1):63–74MathSciNetCrossRefGoogle Scholar
  31. Ni Y, Xie L, Liu Z-Q (2010) Minimizing the expected complete influence time of a social network. Inf Sci 180(13):2514–2527MathSciNetCrossRefMATHGoogle Scholar
  32. Niranjan TT, Wagner SM, Aggarwal V (2011) Measuring information distortion in real-world supply chains. Int J Prod Res 49(11):3343–3362CrossRefGoogle Scholar
  33. Polman E (2010) Information distortion in self-other decision making. J Exp Soc Psychol 46(2):432–435CrossRefGoogle Scholar
  34. 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. ACM, pp 61–70Google Scholar
  35. Russo JE, Meloy MG, Wilks TJ (2000) Predecisional distortion of information by auditors and salespersons. Manag Sci 46:13–27CrossRefGoogle Scholar
  36. Russo EJ, Medvec VH, Meloy MG (1996) The distortion of information during decisions. Organ Behav Hum Decis Process 66(1):102–110CrossRefGoogle Scholar
  37. Russo EJ, Meloy MG, Medvec VH (1998) Predecisional distortion of product information. J Mark Res 35:438–452CrossRefGoogle Scholar
  38. Valente TW (1995) Network models of the diffusion of innovations (quantitative methods in communication series). Hampton Press, New YorkGoogle Scholar
  39. Wang J, Jia J, Takahashi K (2005) A study on the impact of uncertain factors on information distortion in supply chains. Prod Plan Control 16(1):2–11CrossRefGoogle Scholar
  40. Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  41. Watts DJ, Strogatz SH (1998) Collective dynamics of small-worldnetworks. Nature 393(6684):440–442CrossRefMATHGoogle Scholar
  42. Zhang J, Jing X, Chen N, Wang J (2013) Incomplete fingerprint recognition based on feature fusion and pattern entropy. J China Univ Posts Telecommun 20(3):121CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Information Technology and ManagementUniversity of International Business and EconomicsBeijingChina
  2. 2.School of Economics and ManagementTongji UniversityShanghaiChina
  3. 3.School of BusinessRenmin University of ChinaBeijingChina

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