Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory
1
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia
2
Statistical Office of the Republic of Serbia, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(7), 753; https://doi.org/10.3390/e22070753
Received: 15 June 2020
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Revised: 2 July 2020
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Accepted: 3 July 2020
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Published: 9 July 2020
(This article belongs to the Special Issue Theory and Applications of Information Theoretic Machine Learning)
Due to telecommunications market saturation, it is very important for telco operators to always have fresh insights into their customer’s dynamics. In that regard, social network analytics and its application with graph theory can be very useful. In this paper we analyze a social network that is represented by a large telco network graph and perform clustering of its nodes by studying a broad set of metrics, e.g., node in/out degree, first and second order influence, eigenvector, authority and hub values. This paper demonstrates that it is possible to identify some important nodes in our social network (graph) that are vital regarding churn prediction. We show that if such a node leaves a monitored telco operator, customers that frequently interact with that specific node will be more prone to leave the monitored telco operator network as well; thus, by analyzing existing churn and previous call patterns, we proactively predict new customers that will probably churn. The churn prediction results are quantified by using top decile lift metrics. The proposed method is general enough to be readily adopted in any field where homophilic or friendship connections can be assumed as a potential churn driver.
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Keywords:
machine learning; data mining; call data record; churn prediction; graph theory; social network analysis
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MDPI and ACS Style
Kostić, S.M.; Simić, M.I.; Kostić, M.V. Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory. Entropy 2020, 22, 753. https://doi.org/10.3390/e22070753
AMA Style
Kostić SM, Simić MI, Kostić MV. Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory. Entropy. 2020; 22(7):753. https://doi.org/10.3390/e22070753
Chicago/Turabian StyleKostić, Stefan M., Mirjana I. Simić, and Miroljub V. Kostić. 2020. "Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory" Entropy 22, no. 7: 753. https://doi.org/10.3390/e22070753
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