Advertisement

Propagation Models and Analysis for Mobile Phone Data Analytics

  • Derek DoranEmail author
  • Veena Mendiratta
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 85)

Abstract

People in modern society use mobile phones as their primary way to retrieve information and to connect with others across the globe. The kinds of connections these devices support give rise to networks at many levels, from those among devices connected by near-field radio or bluetooth, to society-wide networks of phone calls made between individuals. This chapter introduces state-of-the-art propagation models that have been applied to understand such networks. It discusses how the models are used in many innovative studies, including how short-lived information spreads between phone callers, how malware spreads within public places, how to detect fraudulent and scamming activity on a phone network, and to predict the propensity of a user to unsubscribe from a mobile phone carrier. It concludes with a discussion of future research opportunities for the study of propagation modeling to mobile phone data analytics.

Keywords

Mobile Phone Epidemiological Model Infected Device Influential User Causality Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    800notes: Directory of unknown callers. http://www.whocallsme.com
  2. 2.
    Almeida, T.A., Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of sms spam filtering: new collection and results. In: Proceedings of 11th ACM Symposium on Document Engineering, pp. 259–262. ACM (2011)Google Scholar
  3. 3.
    Anderson, R.M., May, R.M., Anderson, B.: Infectious diseases of humans: dynamics and control, vol. 28. Wiley Online Library (1992)Google Scholar
  4. 4.
    Barron, A., Rissanen, J., Yu, B.: The minimum description length principle in coding and modeling. IEEE Trans. Inf. Theory 44(6), 2743–2760 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Berlingerio, M., Calabrese, F., Di Lorenzo, G., Nair, R., Pinelli, F., Sbodio, M.L.: Allaboard: a system for exploring urban mobility and optimizing public transport using cellphone data. In: Machine Learning and Knowledge Discovery in Databases, pp. 663–666. Springer (2013)Google Scholar
  6. 6.
    Bjelland, J., Canright, G., Engo-Monsen, K., Sundsoy, P.R., Ling, R.S.: A social network study of the apple vs. android smartphone battle. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining, pp. 983–987. IEEE Computer Society (2012)Google Scholar
  7. 7.
    Candia, J., González, M.C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.L.: Uncovering individual and collective human dynamics from mobile phone records. J. Phys. A: Math. Theor. 41, 11 pp (2008)Google Scholar
  8. 8.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)Google Scholar
  9. 9.
    Chien, E.: Security response: Symbos. mabir. Techical report. Symantec Corporation (2005)Google Scholar
  10. 10.
    Corporation, I.: Global business security index report. Technical report. IBM (2004)Google Scholar
  11. 11.
    Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.: Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of 11th ACM International Conference on Extending Database Technology (2008)Google Scholar
  12. 12.
    Doran, D., Alhazmi, H., Gokhale, S.: Triads, transitivity, and social effects in user interactions on facebook. In: Proceedings of IEEE International Conference on Computational Aspects of Social Networks, pp. 68–73 (2013)Google Scholar
  13. 13.
    Doran, D., Mendiratta, V., Phadke, C., Uzunalioglu, H.: The importance of outlier relationships in mobile call graphs. In: Proceedings of International Conference on Machine Learning and Applications, pp. 24–29 (2012)Google Scholar
  14. 14.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  15. 15.
    Ferrie, P., Szor, P., Stanev, R., Mouritzen, R.: Security response: SymbOS. Symantic Corporation, Cabir. Technical repot (2004)Google Scholar
  16. 16.
    Fessenden-Raden, J., Fitchen, J.M., Heath, J.S.: Providing risk information in communities: Factors influencing what is heard and accepted. Sci. Technol. Hum. Values 12, 94–101 (1987)Google Scholar
  17. 17.
    Gleave, E., Welser, H.T., Lento, T.M., Smith, M.A.: A conceptual and operational definition of ‘social role’ in online community. In: 42nd Hawaii International Conference on System Sciences, pp. 1–11 (2009)Google Scholar
  18. 18.
    Groenevelt, R., Nain, P., Koole, G.: The message delay in mobile ad hoc networks. Perform. Eval. 62(1), 210–228 (2005)CrossRefGoogle Scholar
  19. 19.
    Jiang, N., Jin, Y., Skudlark, A., Hsu, W.L., Jacobson, G., Prakasam, S., Zhang, Z.L.: Isolating and analyzing fraud activities in a large cellular network via voice call graph analysis. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 253–266. ACM (2012)Google Scholar
  20. 20.
    Kaspersky: Kaspersky security bulletin malware evolution. Kaspersky Security Bulletin Malware Evolution 2011Google Scholar
  21. 21.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of 9th ACM International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  22. 22.
    Kephart, J.O., White, S.R.: Directed-graph epidemiological models of computer viruses. In: Proceedings of IEEE Computer Society Symposium on Research in Security and Privacy, pp. 343–359. IEEE (1991)Google Scholar
  23. 23.
    Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. part i. Proc. Roy. Soc. Lond. Ser. A 115(5), 700–721 (1927)Google Scholar
  24. 24.
    Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. ii. the problem of endemicity. Proc. Roy. Soc. Lond. Ser. A 138(834), 55–83 (1932)Google Scholar
  25. 25.
    Khouzani, M., Sarkar, S., Altman, E.: Maximum damage malware attack in mobile wireless networks. IEEE/ACM Trans. Netw. 20(5), 1347–1360 (2012)CrossRefGoogle Scholar
  26. 26.
    Kim, H., Zang, H., Ma, X.: Analyzing and modeling temporal patterns of human contacts in cellular networks. In: Proceedings of 22nd IEEE International Conference on Computer Communications and Networks, pp. 1–7 (2013)Google Scholar
  27. 27.
    Kitagawa, G., Gersch, W.: Smoothness Priors Analysis of Time Series, vol. 116. Springer (1996)Google Scholar
  28. 28.
    Krings, G., Calabrese, F., Ratti, C., Blondel, V.D.: Urban gravity: a model for inter-city telecommunication flows. J. Stat. Mech.: Theory Exp. L07003 (2009)Google Scholar
  29. 29.
    Król, D.: Propagation phenomenon in complex networks: theory and practice. New Gener. Comput. 32(3–4), 187–192 (2014)CrossRefGoogle Scholar
  30. 30.
    Kurtz, T.G.: Solutions of ordinary differential equations as limits of pure jump markov processes. J. Appl. Probab. 7(1), 49–58 (1970)CrossRefzbMATHMathSciNetGoogle Scholar
  31. 31.
    Lambiotte, R., Blondel, V.D., de Kerchove, C., Huens, E., Prieur, C., Smoreda, Z., Van Dooren, P.: Geographical dispersal of mobile communication networks. Phys. A Stat. Mech. Appl. 387(21), 5317–5325 (2008)Google Scholar
  32. 32.
    Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM (2007)Google Scholar
  33. 33.
    Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., Tomkins, A.: Geographic routing in social networks. Proc. Natl. Acad. Sci. USA 102(33), 11623–11628 (2005)CrossRefGoogle Scholar
  34. 34.
    Mao, H., Shuai, X., Ahn, Y.Y., Bollen, J.: Mobile communications reveal the regional economy in cote d’ivoire. In: Proceedings of International Conference on Analysis of Mobile Phone Datasets and Networks D4D Book, pp. 1–18 (2013)Google Scholar
  35. 35.
    Mickens, J.W., Noble, B.D.: Modeling epidemic spreading in mobile environments. In: Proceedings of the 4th ACM Workshop on Wireless Security, pp. 77–86. ACM (2005)Google Scholar
  36. 36.
    Montoliu, R., Gatica-Perez, D.: Discovering human places of interest from multimodal mobile phone data. In: Proceedings of 9th International Conference on Mobile and Ubiquitous Multimedia, pp. 12:1–12:10. ACM (2010)Google Scholar
  37. 37.
    Pan, W., Aharony, N., Pentland, A.: Composite social network for predicting mobile apps installation. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 821–827 (2011)Google Scholar
  38. 38.
    Peng, S., Yu, S., Yang, A.: Smartphone malware and its propagation modeling: a survey. IEEE Commun. Surv. Tutor. 16(2), 925–941 (2014)CrossRefGoogle Scholar
  39. 39.
    Peruani, F., Tabourier, L.: Directedness of information flow in mobile phone communication networks. PloS One 6(12), e28,860 (2011)Google Scholar
  40. 40.
    Phadke, C., Mendiratta, V., Uzunalioglu, H., Doran, D.: Prediction of subscriber churn using social network analysis. Bell Labs Tech. J. 17(4), 63–75 (2013)CrossRefGoogle Scholar
  41. 41.
    Phithakkitnukoon, S., Horanont, T., Di Lorenzo, G., Shibasaki, R., Ratti, C.: Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human Behavior Understanding, pp. 14–25. Springer, Berlin (2010)Google Scholar
  42. 42.
    Rhodes, C.J., Nekovee, M.: The opportunistic transmission of wireless worms between mobile devices. Phys. A Stat. Mech. Appl. 387(27), 6837–6844 (2008)CrossRefGoogle Scholar
  43. 43.
    Rivera, M.T., Soderstrom, S.B., Uzzi, B.: Dynamics of dyads in social networks: assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010)CrossRefGoogle Scholar
  44. 44.
    Sellnow, T.L., Ziegelmueller, G.: The persuasive speaking contest: an analysis of twenty years of change. Natl. Forensic J. 6(2), 75–87 (1988)Google Scholar
  45. 45.
    Systems, JJuniper: Mobile Threats Report. In: Technical report (2011)Google Scholar
  46. 46.
    Taillard, M.O.: Persuasive communication: the case of marketing. Working Papers in Linguistics, vol. 12, pp. 145–174 (2000)Google Scholar
  47. 47.
    Trivedi, K.S.: Probability and Statistics with Reliability, Queueing, and Computer Science Applications, 2nd edn. Wiley, New York (2002)Google Scholar
  48. 48.
    Wang, D., Pedreschi, D., Song, C., Giannotti, F., Barabasi, A.L.: Human mobility, social ties, and link prediction. In: Proceedings of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1100–1108. ACM (2011)Google Scholar
  49. 49.
    Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In: Proceedings of 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048. ACM (2010)Google Scholar
  50. 50.
    WhoCallsMe: Reverse phone number lookup. http://www.whocallsme.com
  51. 51.
    Zhang, W., Li, Z., Hu, Y., Xia, W.: Cluster features of bluetooth mobile phone virus and research on strategies of control and prevention. In: Proceedings of International Conference on Computational Intelligence and Security, pp. 474–477. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Kno.e.sis Research CenterWright State UniversityDaytonUSA
  2. 2.Bell LabsAlcatel-LucentNapervilleUSA

Personalised recommendations