Abstract
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.
Notes
This sentence has just been used as an example. However, studies indicate that the most wealthy and affluent people tend to be the most influential in our societies (Easley and Kleinberg 2010).
The reason is described in Sect. 5.
Higgs boson is one of the most elementary elusive particles in modern physics. A meme on Twitter is considered to be a Higgs boson meme if it contains at least one of these keywords or tags: LHC, CERN, boson, Higgs
Homophily is the name given to the tendency of similar people becoming friends with each other. This leads to more number of ties between like-minded people and hence leads to the formation of communities in the network. Social reinforcement is the phenomenon by which multiple exposures of an information to a person lead to him adopting it. Social reinforcement and homophily tend to block the information inside one community
In the case of random network, even though the declared \(10\%\) core nodes have a high probability of infecting their neighbors, the connections between these nodes are not dense enough to result in an overshoot in the number of infected nodes. Therefore, an absence of a distinct core-periphery structure in such networks makes them invalid for our framework.
References
Abrahamson E (1991) Managerial fads and fashions: the diffusion and rejection of innovations. Acad Manag Rev 16(3):586–612
Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230
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 207–214
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 16989–16995
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
Anderson RM, May RM, Anderson B (1992) Infectious diseases of humans: dynamics and control, vol 28. Wiley Online Library, Oxford
Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092):337–341
Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21544–21549
Aral S, Muchnik L, Sundararajan A (2013) Engineering social contagions: optimal network seeding in the presence of homophily. Netw Sci 1(2):125–153
Arnaboldi V, Conti M, Passarella A, Dunbar RI (2017) Online social networks and information diffusion: the role of ego networks. Online Soc Netw Media 1:44–55
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 44–54
Bailey NT et al (1975) The mathematical theory of infectious diseases and its applications. Charles Griffin & Company Ltd, 5a Crendon Street, High Wycombe, Bucks HP13:6LE
Barabási A-L et al (2009) Scale-free networks: a decade and beyond. Science 325(5939):412
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Barrat A, Barthélemy M, Vespignani A (2004) Weighted evolving networks: coupling topology and weight dynamics. Phys Rev Lett 92(22):228701
Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100(5):992–1026
Borgatti SP, Everett MG (2000) Models of core/periphery structures. Soc Netw 21(4):375–395
Brauer F (2008) Compartmental models in epidemiology, in: Mathematical epidemiology, Springer, pp. 19–79
Brown JJ, Reingen PH (1987) Social ties and word-of-mouth referral behavior. J Consumer Res 14(3):350–362
Burt RS (2009) Structural holes: The social structure of competition. Harvard University Press, Cambridge
Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197
Christakis NA, Fowler JH (2007) The spread of obesity in a large social network over 32 years. New England J Med 357(4):370–379
Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111
Daley DJ, Kendall DG (1964) Epidemics and rumours. Nature 204:1118
De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980
Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, Cambridge
Eguiluz VM, Klemm K (2002) Epidemic threshold in structured scale-free networks. Phys Rev Lett 89(10):108701
Erdős P, Rényi A (1961) On the strength of connectedness of a random graph. Acta Mathematica Hungarica 12(1):261–267
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):583–598
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174
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 211–220
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826
Goel S, Anderson A, Hofman J, Watts DJ (2015) The structural virality of online diffusion. Manag Sci 62(1):180–196
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 623–638
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):211–223
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 241–250
Granovetter MS (1973) The strength of weak ties, American journal of sociology 1360–1380
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 491–501
Hein D-IO, Schwind D-W-IM, König W (2006) Scale-free networks. Wirtschaftsinformatik 48(4):267–275
Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653
Huang L, Park K, Lai Y-C (2006) Information propagation on modular networks. Phys Rev E 73(3):035103
Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103(3):038702
Jackson MO, López-Pintado D (2013) Diffusion and contagion in networks with heterogeneous agents and homophily. Netw Sci 1(1):49–67
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 8
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):025102
Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc R Soc Lond Math, Phys Eng Sci 115:700–721
Kim YS, Tran VL (2013) Assessing the ripple effects of online opinion leaders with trust and distrust metrics. Expert Syst Appl 40(9):3500–3511
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):888–893
Kucharavy D, De Guio R (2011) Application of s-shaped curves. Procedia Eng 9:559–572
Kumar R, Novak J, Tomkins A, Structure and evolution of online social networks (2010) 337–357 https://doi.org/10.1007/978-1-4419-6515-8_13
Kunegis J (2013) Konect: the koblenz network collection. In: Proceedings of the 22nd International Conference on World Wide Web, ACM, pp 1343–1350
Leskovec J, Krevl A (2014) SNAP Datasets: Stanford large network dataset collection, http://snap.stanford.edu/data
Leskovec J, Mcauley JJ (2012) Learning to discover social circles in ego networks. In: Advances in neural information processing systems, pp 539–547
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 551–556
Lewis TG (2011) Network science: Theory and applications. Wiley, New York
Liben-Nowell D, Kleinberg J (2008) Tracing information flow on a global scale using internet chain-letter data. Proc Natl Acad Sci 105(12):4633–4638
Luu DM, Lim E-P, Hoang T-A, Chua FCT (2012) Modeling diffusion in social networks using network properties. In: ICWSM
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 125–177
McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415–444
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 1197–1204
Newman ME (2002) Spread of epidemic disease on networks. Phys Rev E 66(1):016128
Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256
Norton JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manag Sci 33(9):1069–1086
Paolillo JC (2001) Language variation on internet relay chat: a social network approach. J Sociolinguistics 5(2):180–213
Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states in complex networks. Phys Rev E 63(6):066117
Pei S, Muchnik L, Andrade JS Jr, Zheng Z, Makse HA (2014) Searching for superspreaders of information in real-world social media. Sci Rep 4:5547
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):e0126894
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):91–106
Petróczi A, Nepusz T, Bazsó F (2007) Measuring tie-strength in virtual social networks. Connections 27(2):39–52
Rogers EM (2010) Diffusion of innovations. Simon and Schuster, New York
Rogers EM, Shoemaker FF (1971) Communication of innovations; a cross-cultural approach. Free Press, New York
Rossa FD, Dercole F, Piccardi C (2013) Profiling core-periphery network structure by random walkers. Sci Rep 3:1467
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):613–635
Sampson RJ (1991) Linking the micro-and macrolevel dimensions of community social organization. Soc Forces 70(1):43–64
Serazzi G, Zanero S (2004) Computer virus propagation models. Performance tools and applications to networked systems. Springer, Berlin, pp 26–50
Shen H-W (2013) Community structure of complex networks. Springer, Berlin
Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771
Weng L, Menczer F, Ahn Y-Y (2013) Virality prediction and community structure in social networks. Sci Rep 3:2522
Weng L, Menczer F, Ahn Y-Y (2014) Predicting successful memes using network and community structure. arXiv preprint arXiv:1403.6199
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):066111
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 981–990
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):2103–2108
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 1633–1636
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 138–147
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gupta, Y., Iyengar, S.R.S., Saxena, A. et al. Modeling memetics using edge diversity. Soc. Netw. Anal. Min. 9, 2 (2019). https://doi.org/10.1007/s13278-018-0546-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-018-0546-6