Social Network Analysis and Mining

, Volume 3, Issue 3, pp 521–541 | Cite as

On information propagation in mobile call networks

  • Kirill DyagilevEmail author
  • Shie Mannor
  • Elad Yom-Tov
Original Article


We consider the dynamics of rapid propagation of information (RPI) in mobile phone networks. We propose a heuristic method for identification of sequences of calls that supposedly propagate the same information and apply it to large-scale real-world data. We show that some of the information propagation events identified by the proposed method can explain the physical co-location of subscribers. We further show that features of subscriber’s behavior in these events can be used for efficient churn prediction. To the best of our knowledge, our method for churn prediction is the first method that relies on dynamic, rather than static, social behavior. Finally, we introduce two generative models that address different aspects of RPI. One model describes the emergence of sequences of calls that lead to RPI. The other model describes the emergence of different topologies of paths in which the information propagates from one subscriber to another. We report high correspondence between certain features observed in the data and these models.


Mobile call network Dynamic behavior of networks Churn prediction 



Preliminary version of this research was published in Dyagilev et al. (2010).


  1. Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, vol 463. Addison-Wesley, New York.
  2. Bi Z, Faloutsos C, Korn F (2001) The “DGX” distribution for mining massive, skewed data. In: KDD’01Google Scholar
  3. Bin L, Peiji S, Juan L (2007) Customer churn prediction based on the decision tree in personal handyphone system service. In: ICSSSM’07Google Scholar
  4. Burez J, Vanden Poel D (2009) Handling class imbalance in customer churn prediction. Expert Syst Appl 36(3):4626–4636CrossRefGoogle Scholar
  5. Catanese S, Ferrara E, Fiumara G (2012) Forensic analysis of phone call networks. Social Netw Anal Min 1–19. ISSN:1869-5450. doi: 10.1007/s13278-012-0060-1
  6. Cormen T, Leiserson C, Rivest R, Stein C (2001) Introduction to algorithms. The MIT press, CambridgeGoogle Scholar
  7. Coussement K, Vanden Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert systems with applications 34(1):313–327CrossRefGoogle Scholar
  8. Dasgupta K, Singh R, Viswanathan B, Chakraborty D, Mukherjea S, Nanavati A, Joshi A (2008) Social ties and their relevance to churn in mobile telecom networks. In: EDBT’08Google Scholar
  9. Datta P, Masand B, Mani D, Li B (2000) Automated cellular modeling and prediction on a large scale. Artif Intell Rev 14(6):485–502zbMATHCrossRefGoogle Scholar
  10. de Melo P, Akoglu L, Faloutsos C, Loureiro A (2010) Surprising patterns for the call duration distribution of mobile phone users. In: PKDD’10Google Scholar
  11. de Oliveira Lima E (2009) Domain knowledge integration in data mining for churn and customer lifetime value modelling: new approaches and applications. PhD thesis, University of SouthamptonGoogle Scholar
  12. Domingos P (2005) Mining social networks for viral marketing. IEEE Intell Syst 20(1):80–82MathSciNetCrossRefGoogle Scholar
  13. Domingos P, Richardson M (2001) Mining the network value of customers. In: KDD’01Google Scholar
  14. Doyle S (2007) The role of social networks in marketing. J Database Mark Cust Strateg Manag 15(1):60–64CrossRefGoogle Scholar
  15. Duda R, Hart P, Stork D (2001) Pattern classification. Wiley, New YorkGoogle Scholar
  16. Dyagilev K, Mannor S, Yom-Tov E (2010) Generative models for rapid information propagation. In: Proceedings of the First Workshop on Social Media Analytics, ACM, pp 35–43Google Scholar
  17. Eagle N, Pentland A, Lazer D (2009) Inferring social network structure using mobile phone data. Proc Nat Acad Sci 106(36):15274–15278CrossRefGoogle Scholar
  18. Fildes R, Kumar V (2002) Telecommunications demand forecasting—a review. Int J Forecast 18(4):489–522CrossRefGoogle Scholar
  19. Gill K (2008) How can we measure the influence of the blogosphere. In: WWW’08Google Scholar
  20. Goldenberg J, Libai B, Moldovan S, Muller E (2007) The NPV of bad news. Int J Res Mark 24(3):186–200CrossRefGoogle Scholar
  21. Goldenberg J, Han S, Lehmann D, Hong J (2009) The role of hubs in the adoption process. J Mark 73(2):1–13CrossRefGoogle Scholar
  22. Gomez Rodriguez M, Leskovec J, Krause A (2010) Inferring networks of diffusion and influence. In: KDD’10Google Scholar
  23. Gopal R, Meher S (2008) Customer churn time prediction in mobile telecommunication industry using ordinal regression. In: Advances in Knowledge Discovery and Data Mining, pp 884–889Google Scholar
  24. Harris T (2002) The theory of branching processes. Dover Publications, New YorkGoogle Scholar
  25. Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276MathSciNetzbMATHCrossRefGoogle Scholar
  26. Jackson M (2008) Social and economic networks. Princeton University Press, PrincetonGoogle Scholar
  27. Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: KDD’03Google Scholar
  28. Kourtellis N, Alahakoon T, Simha R, Iamnitchi A, Tripathi R (2012) Identifying high betweenness centrality nodes in large social networks. Social Netw Anal Min 1–16. ISSN:1869-5450. doi: 10.1007/s13278-012-0076-6
  29. Leskovec J., Adamic L., Huberman B. (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5CrossRefGoogle Scholar
  30. Nanavati A, Gurumurthy S, Das G, Chakraborty D, Dasgupta K, Mukherjea S, Joshi A (2006) On the structural properties of massive telecom call graphs: findings and implications. In: ICIKM’06Google Scholar
  31. NielsenWire (2008) In U.S., SMS text messaging tops mobile phone calling.
  32. Nitzan I, Libai B (2010) Social effects on customer retention, marketing Science Institute, working paper 10-107Google Scholar
  33. Pan W, Aharony N, Pentland A (2011) Composite social network for predicting mobile apps installation. Arxiv preprint arXiv:11060359Google Scholar
  34. Pendharkar P (2009) Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services. Expert Syst Appl 36(3):6714–6720CrossRefGoogle Scholar
  35. Radosavljevik D, van der Putten P, Kyllesbech Larsen K (2010) The impact of experimental setup in prepaid churn prediction for mobile telecommunications: what to predict, for whom and does the customer experience matter? Trans Mach Learn Data Min 3(2):80–99Google Scholar
  36. Richter Y, Yom-Tov E, Slonim N (2010) Predicting customer churn in mobile networks through analysis of social groups. In: ICDM’10Google Scholar
  37. Sadikov E, Medina M, Leskovec J, Garcia-Molina H (2011) Correcting for missing data in information cascades. In: WSDM’11Google Scholar
  38. Saravanan M, Prasad G, Karishma S, Suganthi D (2011) Analyzing and labeling telecom communities using structural properties. Social Netw Anal Min 1(4):271–286. ISSN:1869-5450. doi: 10.1007/s13278-011-0020-1 Google Scholar
  39. Shao J (2003) Mathematical statistics, 2nd edn. Springer, New YorkGoogle Scholar
  40. Song G, Yang D, Wu L, Wang T, Tang S (2006) A mixed process neural network and its application to churn prediction in mobile communications. In: Data Mining Workshop, ICDM’06Google Scholar
  41. Vega-Redondo F (2007) Complex social networks. Cambridge University Press, CambridgeGoogle Scholar
  42. Wu F, Huberman B (2007) Novelty and collective attention. Proc Nat Acad Sci 104(45):17–599CrossRefGoogle Scholar
  43. Yang J, He X, Lee H (2007) Social reference group influence on mobile phone purchasing behaviour: a cross-nation comparative study. Int J Mobile Commun 5(3):319–338CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringTechnionIsrael
  2. 2.IBM Haifa Research LabHaifaIsrael
  3. 3.Microsoft ResearchNew YorkUSA

Personalised recommendations