Churn in Social Networks

By Marcel Karnstedt, Tara Hennessy, Jeffrey Chan, Partha Basuchowdhuri, Conor Hayes, Thorsten Strufe. 2010

34 citations recorded by the source corpus.

Subfields: Information Systems, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Julian McAuley
2. Jure Leskovec
3. Idan Szpektor
4. Gideon Dror
5. Yongfeng Zhang
6. Xiangnan He
7. Philip S. Yu
8. Ruining He

In Marketing

1. Jure Leskovec
2. Gideon Dror
3. Julian McAuley
4. Yongfeng Zhang
5. Xu Chen
6. Philip S. Yu
7. Lei Shu
8. Hu Xu

Citations in the corpus

• From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “This failure to become experienced may be a factor which causes users to abandon the site, and could be used as a feature in ‘churn prediction’ problems [11, 18].”
• Churn prediction in new users of Yahoo! answers (2012, background): “The main approach for churn prediction is to construct a set of features for each user and train a classifier or regressor for the task [6, 12, 15, 7, 4, 14, 18, 11, 13, 2, 3, 10, 17, 8, 9].”