Skip to main content
SpringerLink
Log in

Analyzing and labeling telecom communities using structural properties

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:—rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abello J, Pardalos PM, Resende MGC (1999) On maximum clique problems in very large graphs, In: DIMACS series, 50, American Mathematical Society, pp 119–130

  • Aiello W, Chung F, and Lu L (2000) A random graph model for massive graphs. In: Proceedings of the thirty-second annual ACM symposium on theory of computing, Portland, 21–23 May 2000

  • Bavelas A (1950) Communication patterns in task oriented groups. J Acoust Soc Am 22:271–282

    Article  Google Scholar 

  • Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 10:1–12

    Google Scholar 

  • Boginski V, Butenko S, Pardalos PM (2006) Mining market data: a network approach. Comput Oper Res 33(11):3171–3184

    Article  MATH  Google Scholar 

  • Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177

    Article  MATH  Google Scholar 

  • Candia J, Gonzalez MC, Wang P, Schoenharl T, Madey G, Barabási AL (2008) Uncovering individual and collective human dynamics from mobile phone records. J Phys A Math Theor 41:1–11

    Article  Google Scholar 

  • Carrington P, Scott J, Wasserman S (2005) Models and methods in social network analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Cheung YM (2003) K-means: a new generalized K-means clustering algorithm. Pattern Recognit Lett 24:2883–2893

    Article  MATH  Google Scholar 

  • Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111

    Article  Google Scholar 

  • Dodds PS, Muhumad R, Watts DJ (2003) An experimental study of search in global social networks. Science 301:827–829

    Article  Google Scholar 

  • Doyle S (2008) Social network analysis in the Telco sector—marketing applications. J Database Mark Cust Strategy Manag 15:130–134

    Article  Google Scholar 

  • Ericsson consumer lab (2010) http://www.ericsson.com/thecompany/our-insights/consumerlab

  • Garlaschelli D, Loffredo MI (2004) Patterns of link reciprocity in directed networks. Phys Rev Lett 93:268701

    Article  Google Scholar 

  • Gilbert F, Simonetto P, Zaidi F, Jourdan F, Bourgui R (2010) Communities and hierarchical structures in dynamic social networks: analysis and visualization. In: Social network analysis and mining, online first™, Springer Verlag, 28 October 2010

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. In: Proceedings of the National Academy of Sciences (USA), vol 99. pp 7821–7826

  • Granovetter M (1983) The strength of weak ties: a network theory revisited. Sociol Theor 1:201–223

    Article  Google Scholar 

  • Guimera R, Pardo MS, Amaral LAN (2007) Classes of complex networks defined by role-to-role connectivity profiles. Nat Phys 3(1):63–69

    Article  Google Scholar 

  • Horowitz E, Sahni S (1983) Fundamentals of data structures. Computer Science Press, W H Freeman & Co, New York

  • Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632

    Article  MathSciNet  MATH  Google Scholar 

  • Kumar R, Novak J, Raghavan P, Tomkins A (2004) Structure and evolution of blogspace. CACM 47(12):35–39

    Google Scholar 

  • Li Z, Wang RS, Chen L (2009) Extracting community structure of complex networks by self-organizing maps. In: Proceedings of the third international symposium on optimization and systems biology (OSB’09), Zhangjiajie, China, pp 48–56

  • Monge P, Heiss M, Margolin DB (2008) Communication network evolution in organizational communities. Commun Theor 18(4):449–477

    Article  Google Scholar 

  • Musial K, Kazienko P, Brodka P (2009) User position measures in social network. In: Proceedings of the third SNA-KDD workshop, pp 24–31

  • Nanavati AA, Singh R, Chakraborty D, Dasgupta K, Mukherjea S, Das G, Gurumurthy S, Joshi A (2007) Analyzing the structure and evolution of massive telecom graphs. IEEE Trans Knowl Data Eng 20(5):703–718

    Google Scholar 

  • Newman MEJ (2006) Modularity and community structure in networks, physics/0602124. In: Proceedings of the National Academy of Sciences (USA), vol 103. pp 8577–8582

  • Onnela JP, Saramaki J, Hyvonen J, Szabo G, Lazer D, Kaski K, Kertesz J, Barabási AL (2007) Structure and tie strengths in mobile communication networks. Proc Nat Acad Sci 104(18):7332–7336

    Article  Google Scholar 

  • Proctor CH, Loomis CP (1951) Analysis of sociometric data, research methods in social relations. In: Jahoda M, Deutch M, Cok SW (eds) Dryden Press, New York, pp 561–586

  • Scott J (2011) Social network analysis: developments, advances, and prospects. Soc Netw Anal Min 1(1):21–26

    Article  Google Scholar 

  • Singh L, Getoor L, Licamele L (2005) Pruning social networks using structural properties and descriptive attributes. In: Proceedings of the fifth IEEE international conference on data mining (ICDM’05), pp 773–776

  • Tang L, Liu H (2010) Graph mining applications to social network analysis. Managing and Mining Graph Data, Springer, pp 487–513

    Google Scholar 

  • Wakita K, Tsurumi T (2007) Finding community structure in a mega-scale social networking service. In: Proceedings of IADIS international conference on WWW/Internet, pp 153–162

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, New York

    Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393:440–442

    Article  Google Scholar 

  • Wu S, Wu L, Jin H, Gu S (2007) Customer ranking authority-hub algorithm for mobile communications in China. In: Fourth international conference on fuzzy systems and knowledge discovery FSKD, pp 559–563

Download references

Author information

Authors and Affiliations

  1. Ericsson India Private Limited, Ericsson R&D, Chennai, India

    M. Saravanan, G. Prasad, S. Karishma & D. Suganthi

Authors
  1. M. Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Karishma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Suganthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Saravanan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saravanan, M., Prasad, G., Karishma, S. et al. Analyzing and labeling telecom communities using structural properties. Soc. Netw. Anal. Min. 1, 271–286 (2011). https://doi.org/10.1007/s13278-011-0020-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13278-011-0020-1

Keywords

Search

Navigation