Skip to main content
SpringerLink
Log in

Propagation Models and Analysis for Mobile Phone Data Analytics

  • Chapter
  • First Online:
Propagation Phenomena in Real World Networks

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 85))

  • 985 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats.

References

  1. 800notes: Directory of unknown callers. http://www.whocallsme.com

  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. Anderson, R.M., May, R.M., Anderson, B.: Infectious diseases of humans: dynamics and control, vol. 28. Wiley Online Library (1992)

    Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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. 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. 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. Chien, E.: Security response: Symbos. mabir. Techical report. Symantec Corporation (2005)

    Google Scholar 

  10. Corporation, I.: Global business security index report. Technical report. IBM (2004)

    Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  15. Ferrie, P., Szor, P., Stanev, R., Mouritzen, R.: Security response: SymbOS. Symantic Corporation, Cabir. Technical repot (2004)

    Google Scholar 

  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. 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. Groenevelt, R., Nain, P., Koole, G.: The message delay in mobile ad hoc networks. Perform. Eval. 62(1), 210–228 (2005)

    Article  Google Scholar 

  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. Kaspersky: Kaspersky security bulletin malware evolution. Kaspersky Security Bulletin Malware Evolution 2011

    Google Scholar 

  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. 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. 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. 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. Khouzani, M., Sarkar, S., Altman, E.: Maximum damage malware attack in mobile wireless networks. IEEE/ACM Trans. Netw. 20(5), 1347–1360 (2012)

    Article  Google Scholar 

  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. Kitagawa, G., Gersch, W.: Smoothness Priors Analysis of Time Series, vol. 116. Springer (1996)

    Google Scholar 

  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. Król, D.: Propagation phenomenon in complex networks: theory and practice. New Gener. Comput. 32(3–4), 187–192 (2014)

    Article  Google Scholar 

  30. Kurtz, T.G.: Solutions of ordinary differential equations as limits of pure jump markov processes. J. Appl. Probab. 7(1), 49–58 (1970)

    Article  MATH  MathSciNet  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. Peng, S., Yu, S., Yang, A.: Smartphone malware and its propagation modeling: a survey. IEEE Commun. Surv. Tutor. 16(2), 925–941 (2014)

    Article  Google Scholar 

  39. Peruani, F., Tabourier, L.: Directedness of information flow in mobile phone communication networks. PloS One 6(12), e28,860 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Rhodes, C.J., Nekovee, M.: The opportunistic transmission of wireless worms between mobile devices. Phys. A Stat. Mech. Appl. 387(27), 6837–6844 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Systems, JJuniper: Mobile Threats Report. In: Technical report (2011)

    Google Scholar 

  46. Taillard, M.O.: Persuasive communication: the case of marketing. Working Papers in Linguistics, vol. 12, pp. 145–174 (2000)

    Google Scholar 

  47. Trivedi, K.S.: Probability and Statistics with Reliability, Queueing, and Computer Science Applications, 2nd edn. Wiley, New York (2002)

    Google Scholar 

  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. 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. WhoCallsMe: Reverse phone number lookup. http://www.whocallsme.com

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kno.e.sis Research Center, Wright State University, Dayton, OH, 45435, USA

    Derek Doran

  2. Bell Labs, Alcatel-Lucent, Naperville, IL, 60563, USA

    Veena Mendiratta

Authors
  1. Derek Doran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Veena Mendiratta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Derek Doran .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Technology, Wrocław, Poland

    Dariusz Król

  2. School of Design, Engineering and Computing, Bournemouth University, Fern Barrow Poole, United Kingdom

    Damien Fay

  3. Computational Intelligence Research Group, Bournemouth University Smart Technology Research Centre, Poole, United Kingdom

    Bogdan GabryÅ›

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Doran, D., Mendiratta, V. (2015). Propagation Models and Analysis for Mobile Phone Data Analytics. In: Król, D., Fay, D., Gabryś, B. (eds) Propagation Phenomena in Real World Networks. Intelligent Systems Reference Library, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-15916-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15916-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15915-7

  • Online ISBN: 978-3-319-15916-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation