Inductive Representation Learning on Feature Rich Complex Networks for Churn Prediction in Telco

  • María ÓskarsdóttirEmail author
  • Sander Cornette
  • Floris Deseure
  • Bart Baesens
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


In the mobile telecommunication industry, call networks have been used with great success to predict customer churn. These social networks are complex and rich in features, because the telecommunications operators have a lot of information about their customers. In this paper we leverage a novel framework called GraphSAGE for inductive representation learning on networks with the goal of predicting customer churn. The technique has an advantage over previously proposed representation learning techniques because it leverages node features in the learning process. It also features a supervised learning process, which can be used to predict churn directly, as well as an unsupervised variant which produces an embedding. We study how the number of node features impacts the predictive performance of churn models as well as the benefit of a complete learning process, compared to an embedding with supervised machine learning techniques. Finally, we compare the performance of GraphSAGE to that of standard local models.


Call network Churn prediction Representation learning Supervised learning 


  1. 1.
    De Caigny, A., Coussement, K., De Bock, K.W.: A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur. J. Oper. Res. 269(2), 760–772 (2018)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: 2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)Google Scholar
  3. 3.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)CrossRefGoogle Scholar
  4. 4.
    Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 2017, pp. 1024–1034 (2017)Google Scholar
  5. 5.
    Kim, K., Jun, C.H., Lee, J.: Improved churn prediction in telecommunication industry by analyzing a large network. Expert Syst. Appl. 41(15), 6575–6584 (2014)CrossRefGoogle Scholar
  6. 6.
    Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., Vanthienen, J.: Social network analytics for churn prediction in telco: model building, evaluation and network architecture. Expert Syst. Appl. 85, 204–20 (2017)CrossRefGoogle Scholar
  7. 7.
    Óskarsdóttir, M., Van Calster, T., Baesens, B., Lemahieu, W., Vanthienen, J.: Time series for early churn detection: using similarity based classification for dynamic networks. Expert Syst. Appl. 106, 55–65 (2018)CrossRefGoogle Scholar
  8. 8.
    Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: 2014 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)Google Scholar
  9. 9.
    Verbeke, W., Martens, D., Baesens, B.: Social network analysis for customer churn prediction. Appl. Soft Comput. 14, 431–46 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • María Óskarsdóttir
    • 1
    Email author
  • Sander Cornette
    • 2
  • Floris Deseure
    • 2
  • Bart Baesens
    • 2
    • 3
  1. 1.Department of Computer ScienceReykjavík UniversityReykjavíkIceland
  2. 2.Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium
  3. 3.Department of Decision Analytics and RiskUniversity of SouthamptonSouthamptonUK

Personalised recommendations