Clique Comparison and Homophily Detection in Telecom Social Networks

Social Network Analysis (SNA) is based on graph theory and is used for identification of the structure, behavioral patterns and social connectivity of entities. In this paper, SNA is used in the telecom industry in terms of a call detail record referring to phone call data separated into two groups, i.e., domicile network and virtual operator network data. Emphasis was placed on community detection. Comparison was made among communities detected in domicile and virtual operator networks. Results show that in contrast to domicile network, the number of cliques in the virtual operator network is larger. Also, homophily was detected between domicile network and virtual operator network users.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  Lucas Antiqueira,et al.  Analyzing and modeling real-world phenomena with complex networks: a survey of applications , 2007, 0711.3199.

[3]  Bart Baesens,et al.  Combining local and social network classifiers to improve churn prediction , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[4]  Pushpa An Efficient Method of Building the Telecom Social Network for Churn Prediction , 2012 .

[5]  Sougata Mukherjea,et al.  Discovery and analysis of tightly knit communities in telecom social networks , 2010, IBM J. Res. Dev..

[6]  Ivan Boban,et al.  Performance Comparison of Machine Learning Methods for Customer Churn Prediction in Telecom , 2019 .

[7]  Pushpa Ravikumar,et al.  Telecommunication community detection by decomposing network into n-cliques , 2016, 2016 International Conference on Emerging Technological Trends (ICETT).

[8]  Robert A. Hanneman,et al.  Concepts and Measures for Basic Network Analysis , 2014 .

[9]  João Gama,et al.  An overview of social network analysis , 2012, WIREs Data Mining Knowl. Discov..

[10]  Cees T. A. M. de Laat,et al.  Defining architecture components of the Big Data Ecosystem , 2014, 2014 International Conference on Collaboration Technologies and Systems (CTS).

[11]  Markus Helfert,et al.  Mixing Scores from Artificial Neural Network and Social Network Analysis to Improve the Customer Loyalty , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[12]  Gian Paolo Rossi,et al.  Calling and Texting: Social Interactions in a Multidimensional Telecom Graph , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[13]  Pushpa Ravikumar,et al.  Community Mining in Multi-relational and Heterogeneous Telecom Network , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[14]  Francis J. Flynn,et al.  Do you two know each other? Transitivity, homophily, and the need for (network) closure. , 2010, Journal of personality and social psychology.

[15]  Ye Ouyang,et al.  Mining of leaders in mobile telecom social networks , 2016, 2016 Wireless Telecommunications Symposium (WTS).

[16]  Kevin Lü,et al.  Detecting Communities with Different Sizes for Social Network Analysis , 2015, Comput. J..