Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Abstract

<jats:p>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.</jats:p>

Model Optimization Analysis of Customer Churn Prediction Using Machine Learning Algorithms with Focus on Feature Reductions

“…Li et al [26] developed a unique tutorial that uses the viewpoint of pre-coding design in a multiantenna wireless communication system to address interference exploitation strategies. Kostić et al [27] used graph theory for churn analysis of telecom companies from the social network. Customer friendship and connection in social media are also effective in churn prediction in their model.…”

“…e study of principal components is one of the outcomes of linear algebra mathematics because the nonparametric and straightforward method extracts relevant information from confusing sets. e transformation of the T can be obtained by minimizing the least-squares error, assuming that the CCPBI-TAMO, CPIO-FS Telecom Precision, recall, accuracy, F-Score, ROC [41] Xgboost, AdaBoost, catboost, decision trees, SVM, KNN Telecom Accuracy, AUC, precision, recall, F-Measures [37] Deep feed-forward networks Subscription companies Accuracy [38] Deep ANN, machine learning algorithms Telecom Accuracy, precision, recall, F1-score, and AUC [12] Neural network with bagging Telecom Accuracy, precision, recall, F-score, kappa, absolute error, relative error, and classi cation error [10] Transfer learning of ensemble Telecom Area under curve of ROC (AUC) and complexity [11] Ensemble algorithm Telecom Area under curve of ROC (AUC) [12] Begging and neural network Telecom Accuracy and precision of classi cation [42] Arti cial neural network (ANN) and self-organized map (SOM) Telecom Accuracy, recall, F-score, and precision [15] Pro t tree Telecom Accuracy, cost, and pro t [16] Minimax probability machines Telecom AUC and EMPC [17] similarity forests Telecom AUC, and tenlift AUPR [21] Temporal point processes (TPP) and recurrent neural networks (RNN) Telecom MAE and MRE [22] Cross-company just-in-time approach Telecom Accuracy, Kappa, and Recall [25] Multiobjective and colony optimization Telecom AUC [27] graph theory Telecom Top decile lift [31] Boosted…”

Churn Prediction Model Improvement Using Automated Machine Learning with Social Network Parameters

“…SNA is a set of analytic methods used to show and measure connectivity and interaction between people, groups, organizations, computers and other connectable entities [15]. Common usage of an SNA in the telecom industry involves the identification of the most influential users in the telecom network [16], the identification of communities and groups [17], fraud detection [18], tariff model recommendation system [19] and predicting telecom churn [8,[20][21][22].…”

“…They also observed that the user's churn depends on the previous behaviour of his neighbours. Kostic et al [22] created a churn prediction model using SNA in combination with clustering. They identified some important nodes in the telecom social network that are vital regarding churn prediction.…”

Markov Chain: A Novel Tool for Electronic Ripple Analysis

“…The design and construction of electronic circuits necessitate the development of novel techniques for analysing the signals that introduced the field of signal processing, dealing with the synthesis and analysis of electrical, electronic, sound, image, and video signals (1,2) .The popular signalprocessing techniques include fast Fourier transform, wavelet, nonlinear time series, complex network, and functional analyses (3) . The nature of the signal and the intended result decide the technique to be employed.…”