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Inductive representation learning on feature rich complex networks for Churn prediction in Telco

Inductive representation learning on feature rich complex networks for Churn prediction in Telco
Inductive representation learning on feature rich complex networks for Churn prediction in Telco
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.
1860-949X
845-853
Springer
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Cornette, Sander
1d5d39c6-0c7d-42e2-a3df-63fd3972a672
Deseure, Floris
26ff40e0-786f-4f26-98fb-77797b16598a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Cherifi, H.
Gaito, S.
Mendes, J.
Moro, E.
Rocha, L.
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Cornette, Sander
1d5d39c6-0c7d-42e2-a3df-63fd3972a672
Deseure, Floris
26ff40e0-786f-4f26-98fb-77797b16598a
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Cherifi, H.
Gaito, S.
Mendes, J.
Moro, E.
Rocha, L.

Óskarsdóttir, María, Cornette, Sander, Deseure, Floris and Baesens, Bart (2020) Inductive representation learning on feature rich complex networks for Churn prediction in Telco. Cherifi, H., Gaito, S., Mendes, J., Moro, E. and Rocha, L. (eds.) In International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2019: Complex Networks and Their Applications VIII. vol. 881, Springer. pp. 845-853 . (doi:10.1007/978-3-030-36687-2_70).

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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graphSageChurn - Accepted Manuscript
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Accepted/In Press date: 17 October 2019
e-pub ahead of print date: 26 November 2019
Published date: 2020

Identifiers

Local EPrints ID: 437099
URI: http://eprints.soton.ac.uk/id/eprint/437099
ISSN: 1860-949X
PURE UUID: f5e498bd-5cc2-4918-aa2d-e0312f9f4e0b
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 17 Jan 2020 17:30
Last modified: 27 Jan 2020 13:40

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