Modeling memetics using edge diversity

  • Yayati GuptaEmail author
  • S. R. S. Iyengar
  • Akrati Saxena
  • Debarati Das
Original Article


The diffusion of an idea significantly differs from the diffusion of a disease because of the interplay of the complex sociological and behavioral factors in the former. Hence, the conventional epidemiological models fail to capture the heterogeneity of social networks and the complexity of information diffusion. Standard information diffusion models depend heavily on the micro-level parameters of the network like edge weights and implicit vulnerabilities of nodes towards information. Such parameters are rarely available because of the absence of large amounts of information diffusion data. Hence, modeling information diffusion remains a challenging research problem. In this paper, we utilize the peculiar structure of the real-world social networks to derive useful insights into the micro-level parameters. We propose an artificial framework mimicking the real-world information diffusion. The framework includes (1) a synthetic network which structurally resembles a real-world social network and (2) a meme spreading model based on the penta-level classification of edges in the network. The experimental results prove that the synthetic network combined with the proposed spreading model is able to simulate a real-world meme diffusion. The framework is validated with the help of the diffusion data of the Higgs boson meme on Twitter and the datasets of several popular real-world social networks.


Information diffusion model Higgs boson Core-periphery structure Community structure Scale-free networks 


  1. Abrahamson E (1991) Managerial fads and fashions: the diffusion and rejection of innovations. Acad Manag Rev 16(3):586–612CrossRefGoogle Scholar
  2. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230CrossRefGoogle Scholar
  3. Adar E, Adamic LA (2005) Tracking information epidemics in blogspace. In: Proceedings of the 2005 IEEE/WIC/ACM international conference on web intelligence, IEEE Computer Society, pp 207–214Google Scholar
  4. Adar E, Zhang L, Adamic LA, Lukose RM (2004) Implicit structure and the dynamics of blogspace. In: Workshop on the weblogging ecosystem, Vol. 13, pp 16989–16995Google Scholar
  5. Alvarez-Hamelin JI, Dall’Asta L, Barrat A, Vespignani A (2005) k-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases. arXiv preprint arXiv:cs/0511007
  6. Anderson RM, May RM, Anderson B (1992) Infectious diseases of humans: dynamics and control, vol 28. Wiley Online Library, OxfordGoogle Scholar
  7. Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092):337–341MathSciNetCrossRefGoogle Scholar
  8. Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21544–21549CrossRefGoogle Scholar
  9. Aral S, Muchnik L, Sundararajan A (2013) Engineering social contagions: optimal network seeding in the presence of homophily. Netw Sci 1(2):125–153CrossRefGoogle Scholar
  10. Arnaboldi V, Conti M, Passarella A, Dunbar RI (2017) Online social networks and information diffusion: the role of ego networks. Online Soc Netw Media 1:44–55CrossRefGoogle Scholar
  11. Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution, in: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 44–54Google Scholar
  12. Bailey NT et al (1975) The mathematical theory of infectious diseases and its applications. Charles Griffin & Company Ltd, 5a Crendon Street, High Wycombe, Bucks HP13:6LEGoogle Scholar
  13. Barabási A-L et al (2009) Scale-free networks: a decade and beyond. Science 325(5939):412MathSciNetCrossRefGoogle Scholar
  14. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefGoogle Scholar
  15. Barrat A, Barthélemy M, Vespignani A (2004) Weighted evolving networks: coupling topology and weight dynamics. Phys Rev Lett 92(22):228701CrossRefGoogle Scholar
  16. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100(5):992–1026CrossRefGoogle Scholar
  17. Borgatti SP, Everett MG (2000) Models of core/periphery structures. Soc Netw 21(4):375–395CrossRefGoogle Scholar
  18. Brauer F (2008) Compartmental models in epidemiology, in: Mathematical epidemiology, Springer, pp. 19–79Google Scholar
  19. Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consumer Res 14(3):350–362CrossRefGoogle Scholar
  20. Burt RS (2009) Structural holes: The social structure of competition. Harvard University Press, CambridgeGoogle Scholar
  21. Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197CrossRefGoogle Scholar
  22. Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. New England J Med 357(4):370–379CrossRefGoogle Scholar
  23. Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111CrossRefGoogle Scholar
  24. Daley DJ, Kendall DG (1964) Epidemics and rumours. Nature 204:1118CrossRefGoogle Scholar
  25. De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980CrossRefGoogle Scholar
  26. Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  27. Eguiluz VM, Klemm K (2002) Epidemic threshold in structured scale-free networks. Phys Rev Lett 89(10):108701CrossRefGoogle Scholar
  28. Erdős P, Rényi A (1961) On the strength of connectedness of a random graph. Acta Mathematica Hungarica 12(1):261–267MathSciNetzbMATHGoogle Scholar
  29. Erez M, Gati E (2004) A dynamic, multi-level model of culture: from the micro level of the individual to the macro level of a global culture. Appl Psychol 53(4):583–598CrossRefGoogle Scholar
  30. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  31. Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 211–220Google Scholar
  32. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefGoogle Scholar
  33. Goel S, Anderson A, Hofman J, Watts DJ (2015) The structural virality of online diffusion. Manag Sci 62(1):180–196Google Scholar
  34. Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on electronic commerce, ACM, pp 623–638Google Scholar
  35. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market Lett 12(3):211–223CrossRefGoogle Scholar
  36. Goyal A, Bonchi F, Lakshmanan LV (2010) Learning influence probabilities in social networks. In: Proceedings of the third ACM international conference on Web search and data mining, ACM, pp 241–250Google Scholar
  37. Granovetter MS (1973) The strength of weak ties, American journal of sociology 1360–1380CrossRefGoogle Scholar
  38. Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web, ACM, pp 491–501Google Scholar
  39. Hein D-IO, Schwind D-W-IM, König W (2006) Scale-free networks. Wirtschaftsinformatik 48(4):267–275CrossRefGoogle Scholar
  40. Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653MathSciNetCrossRefGoogle Scholar
  41. Huang L, Park K, Lai Y-C (2006) Information propagation on modular networks. Phys Rev E 73(3):035103CrossRefGoogle Scholar
  42. Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103(3):038702CrossRefGoogle Scholar
  43. Jackson MO, López-Pintado D (2013) Diffusion and contagion in networks with heterogeneous agents and homophily. Netw Sci 1(1):49–67CrossRefGoogle Scholar
  44. Jin F, Dougherty E, Saraf P, Cao Y, Ramakrishnan N (2013) Epidemiological modeling of news and rumors on twitter, in:Proceedings of the 7th Workshop on Social Network Mining and Analysis, ACM, p 8Google Scholar
  45. Karsai M, Kivelä M, Pan RK, Kaski K, Kertész J, Barabási A-L, Saramäki J (2011) Small but slow world: how network topology and burstiness slow down spreading. Phys Rev E 83(2):025102CrossRefGoogle Scholar
  46. Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc R Soc Lond Math, Phys Eng Sci 115:700–721CrossRefGoogle Scholar
  47. Kim YS, Tran VL (2013) Assessing the ripple effects of online opinion leaders with trust and distrust metrics. Expert Syst Appl 40(9):3500–3511CrossRefGoogle Scholar
  48. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893CrossRefGoogle Scholar
  49. Kucharavy D, De Guio R (2011) Application of s-shaped curves. Procedia Eng 9:559–572CrossRefGoogle Scholar
  50. Kumar R, Novak J, Tomkins A, Structure and evolution of online social networks (2010) 337–357 CrossRefGoogle Scholar
  51. Kunegis J (2013) Konect: the koblenz network collection. In: Proceedings of the 22nd International Conference on World Wide Web, ACM, pp 1343–1350Google Scholar
  52. Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection,
  53. Leskovec J, Mcauley JJ (2012) Learning to discover social circles in ego networks. In: Advances in neural information processing systems, pp 539–547Google Scholar
  54. Leskovec J, McGlohon M, Faloutsos C, Glance NS, Hurst M (2007) Patterns of cascading behavior in large blog graphs. In: SDM, Vol. 7, SIAM, pp 551–556Google Scholar
  55. Lewis TG (2011) Network science: Theory and applications. Wiley, New YorkGoogle Scholar
  56. Liben-Nowell D, Kleinberg J (2008) Tracing information flow on a global scale using internet chain-letter data. Proc Natl Acad Sci 105(12):4633–4638CrossRefGoogle Scholar
  57. Luu DM, Lim E-P, Hoang T-A, Chua FCT (2012) Modeling diffusion in social networks using network properties. In: ICWSMGoogle Scholar
  58. Mahajan V, Muller E, Bass FM (1991) New product diffusion models in marketing: a review and directions for research. In: Diffusion of technologies and social behavior, Springer, pp 125–177Google Scholar
  59. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415–444CrossRefGoogle Scholar
  60. Najar A, Denoyer L, Gallinari P (2012) Predicting information diffusion on social networks with partial knowledge. In: Proceedings of the 21st International Conference on World Wide Web, ACM, pp 1197–1204Google Scholar
  61. Newman ME (2002) Spread of epidemic disease on networks. Phys Rev E 66(1):016128MathSciNetCrossRefGoogle Scholar
  62. Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256MathSciNetCrossRefGoogle Scholar
  63. Norton JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manag Sci 33(9):1069–1086CrossRefGoogle Scholar
  64. Paolillo JC (2001) Language variation on internet relay chat: a social network approach. J Sociolinguistics 5(2):180–213CrossRefGoogle Scholar
  65. Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states in complex networks. Phys Rev E 63(6):066117CrossRefGoogle Scholar
  66. Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA (2014) Searching for superspreaders of information in real-world social media. Sci Rep 4:5547CrossRefGoogle Scholar
  67. Pei S, Muchnik L, Tang S, Zheng Z, Makse HA (2015) Exploring the complex pattern of information spreading in online blog communities. PloS One 10(5):e0126894CrossRefGoogle Scholar
  68. Peres R, Muller E, Mahajan V (2010) Innovation diffusion and new product growth models: a critical review and research directions. Int J Res Market 27(2):91–106CrossRefGoogle Scholar
  69. Petróczi A, Nepusz T, Bazsó F (2007) Measuring tie-strength in virtual social networks. Connections 27(2):39–52Google Scholar
  70. Rogers EM (2010) Diffusion of innovations. Simon and Schuster, New YorkGoogle Scholar
  71. Rogers EM, Shoemaker FF (1971) Communication of innovations; a cross-cultural approach. Free Press, New YorkGoogle Scholar
  72. Rossa FD, Dercole F, Piccardi C (2013) Profiling core-periphery network structure by random walkers. Sci Rep 3:1467CrossRefGoogle Scholar
  73. Saito K, Kimura M, Ohara K, Motoda H (2012) Efficient discovery of influential nodes for sis models in social networks. Knowl Inf Syst 30(3):613–635CrossRefGoogle Scholar
  74. Sampson RJ (1991) Linking the micro-and macrolevel dimensions of community social organization. Soc Forces 70(1):43–64CrossRefGoogle Scholar
  75. Serazzi G, Zanero S (2004) Computer virus propagation models. Performance tools and applications to networked systems. Springer, Berlin, pp 26–50CrossRefGoogle Scholar
  76. Shen H-W (2013) Community structure of complex networks. Springer, BerlinCrossRefGoogle Scholar
  77. Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771MathSciNetCrossRefGoogle Scholar
  78. Weng L, Menczer F, Ahn Y-Y (2013) Virality prediction and community structure in social networks. Sci Rep 3:2522CrossRefGoogle Scholar
  79. Weng L, Menczer F, Ahn Y-Y (2014) Predicting successful memes using network and community structure. arXiv preprint arXiv:1403.6199
  80. Wu J-J, Gao Z-Y, Sun H-J (2006) Cascade and breakdown in scale-free networks with community structure. Phys Rev E 74(6):066111CrossRefGoogle Scholar
  81. Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of the 19th international conference on world wide web. ACM, pp 981–990Google Scholar
  82. Xiong F, Liu Y, Zhang Z-J, Zhu J, Zhang Y (2012) An information diffusion model based on retweeting mechanism for online social media. Phys Lett A 376(30–31):2103–2108CrossRefGoogle Scholar
  83. Yang Z, Guo J, Cai K, Tang J, Li J, Zhang L, Su Z (2010) Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM international conference on Information and knowledge management, ACM, pp 1633–1636Google Scholar
  84. Zou CC, Gong W, Towsley D (2002) Code red worm propagation modeling and analysis. In: Proceedings of the 9th ACM conference on Computer and communications security, ACM, pp 138–147Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Yayati Gupta
    • 1
    Email author
  • S. R. S. Iyengar
    • 1
  • Akrati Saxena
    • 1
  • Debarati Das
    • 2
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology RoparRupnagarIndia
  2. 2.Department of Computer ScienceUniversity of MinnesotaMinneapolisUSA

Personalised recommendations