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Stochastic Modeling of the Decay Dynamics of Online Social Networks

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Complex Networks VIII (CompleNet 2017)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

The dynamics of online social networks (OSNs) involves a complicated mixture of growth and decay. In the last decade, many online social networks, like MySpace and Orkut, suffered from decay until they were too small to sustain themselves. Thus, understanding this decay process is crucial for many scenarios that include: (1) Engineering a resilient network, (2) Accelerating the disruption of malicious network structures, and (3) Predicting users leave dynamics. In this work we are interested in modeling and understanding the decay dynamics in OSNs to handle the aforementioned three scenarios. Here, we present a probabilistic model that captures the dynamics of the social decay due to the inactivity of the members in a social network. The model is proved to have submodularity property. We provide preliminary results and analyse some properties of real networks under decay process and compare it to the model’s results. The results show, at the macro level of the networks, that there is a match between the properties of the decaying real networks and the model.

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Notes

  1. 1.

    Detailed proofs are provided in an earlier technical paper [1].

References

  1. Abufouda, M., Zweig, K.A.: A theoretical model for understanding the dynamics of online social networks decay (2016). arXiv:1610.01538

  2. Ahn, Y.-Y., Han, S., Kwak, H., Moon, S., Jeong, H.: Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th International Conference on WWW, pp. 835–844. ACM (2007)

    Google Scholar 

  3. Asur, S., Huberman, B.A., Szabo, G., Wang, C.: Trends in Social Media: Persistence and Decay (2011). SSRN 1755748

    Google Scholar 

  4. Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD, pp. 44–54. ACM (2006)

    Google Scholar 

  5. Bala, V., Goyal, S.: A noncooperative model of network formation. Econometrica 68(5), 1181–1229 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  6. Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Am. Assoc. Adv. Sci. 286(5439), 509–512 (1999)

    MathSciNet  MATH  Google Scholar 

  7. Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks (2003). arXiv:cs/0310049

  8. Bhawalkar, K., Kleinberg, J., Lewi, K., Roughgarden, T., Sharma, A.: Preventing unraveling in social networks: the anchored k-core problem. SIAM J. Discrete Math. 29(3), 1452–1475 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  9. Capocci, A., et al.: Preferential attachment in the growth of social networks: the internet encyclopedia wikipedia. Phys. Rev. E 74(3), 036116 (2006)

    Article  ADS  Google Scholar 

  10. Chhabra, S.S., Brundavanam, A., Shannigrahi, S.: An alternative explanation for the rise and fall of MySpace (2014). arXiv:1403.5617

  11. Dorogovtsev, S.N., Mendes, J.F.F.: Scaling behaviour of developing and decaying networks. EPL (Europhys. Lett.) 52(1), 33 (2000)

    Article  ADS  Google Scholar 

  12. Garcia, D., Mavrodiev, P., Schweitzer, F.: Social resilience in online communities: the autopsy of Friendster. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 39–50. ACM (2013)

    Google Scholar 

  13. Iwata, S., Fleischer, L., Fujishige, S.: A combinatorial strongly polynomial algorithm for minimizing submodular functions. J. ACM (JACM) 48(4), 761–777 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  14. Jackson, M.O.: A survey of network formation models: stability and efficiency. In: Group Formation in Economics: Networks, Clubs, and Coalitions, pp. 11–49 (2003)

    Google Scholar 

  15. Jin, E.M., Girvan, M., Newman, M.E.: Structure of growing social networks. Phys. Rev. E 64(4) (2001)

    Google Scholar 

  16. Kairam, S.R., Wang, D.J., Leskovec, J.: The life and death of online groups: predicting group growth and longevity. In: Proceedings of the Fifth International Conference on Web Search and Data Mining, pp. 673–682. ACM (2012)

    Google Scholar 

  17. Kordestani, A.A., Limayem, M., Salehi-Sangari, E., Blomgren, H., Afsharipour, A.: Why a few social networking sites succeed while many fail. In: The Sustainable Global Marketplace, pp. 283–285. Springer (2015)

    Google Scholar 

  18. Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  19. Krause, A., Golovin, D.: Submodular function maximization. In: Tractability: Practical Approaches to Hard Problems (2012)

    Google Scholar 

  20. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 611–617 (2006)

    Google Scholar 

  21. Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD, pp. 177–187. ACM (2005)

    Google Scholar 

  22. Malliaros, F.D., Vazirgiannis, M.: To stay or not to stay: modeling engagement dynamics in social graphs. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, CIKM’13, pp. 469–478. ACM, New York, NY, USA (2013)

    Google Scholar 

  23. Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Growth of the flickr social network. In: Proceedings of the First Workshop on Online Social Networks, pp. 25–30. ACM (2008)

    Google Scholar 

  24. Nemhauser, G.L., Wolsey, L.A.: Best algorithms for approximating the maximum of a submodular set function. Math. Oper. Res. 3(3), 177–188 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  25. Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)

    Article  ADS  Google Scholar 

  26. Ribeiro, B.: Modeling and predicting the growth and death of membership-based websites. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 653–664. ACM (2014)

    Google Scholar 

  27. Stieger, S., Burger, C., Bohn, M., Voracek, M.: Who commits virtual identity suicide? Differences in privacy concerns, internet addiction, and personality between facebook users and quitters. Cyberpsychol. Behav. Soc. Netw. 16(9), 629–634 (2013)

    Article  Google Scholar 

  28. Torkjazi, M., Rejaie, R., Willinger, W.: Hot today, gone tomorrow: on the migration of MySpace users. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 43–48. ACM (2009)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, University of Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663, Kaiserslautern, Germany

    Mohammed Abufouda & Katharina A. Zweig

Authors
  1. Mohammed Abufouda
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  2. Katharina A. Zweig
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Corresponding author

Correspondence to Mohammed Abufouda .

Editor information

Editors and Affiliations

  1. New York University, Center for Data Science New York University, New York, New York, USA

    Bruno Gonçalves

  2. BioComplex Laboratory, School of Computing, Florida Institute of Technology, Melbourne, Florida, USA

    Ronaldo Menezes

  3. Center for Network Science and Mathematics Department, Central European University, Budapest, Hungary

    Roberta Sinatra

  4. Rudjer Bošković Institute, Theoretical Physics Division, Zagreb, Croatia

    Vinko Zlatic

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Abufouda, M., Zweig, K.A. (2017). Stochastic Modeling of the Decay Dynamics of Online Social Networks. In: Gonçalves, B., Menezes, R., Sinatra, R., Zlatic, V. (eds) Complex Networks VIII. CompleNet 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-54241-6_10

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