Social ties and their relevance to churn in mobile telecom networks
By Koustuv Dasgupta, Rahul Singh, Balaji Viswanathan, Dipanjan Chakraborty, Sougata Mukherjea, Amit Anil Nanavati, Anupam Joshi. International Conference on Extending Database Technology, 2008
369 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/d98efe399ca78c8b28f469fcc1262ab62c458cd9
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In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, background): “Alternatively, social network information extracted from call detail records can be explored to predict churn (Dasgupta et al., 2008), which is especially interesting if no other information is available.”
- Link Prediction in Complex Networks: A Survey (2010, background): “on of protein functions [35], the detection of anomalous email [122], distinguishing the research ares of scientific publications [123], and finding out the fraud and legit users in cell phone networks [124]. The following three subsections will introduce typical applications of link prediction. 6.1 R”
- Churn in Social Networks (2010, methodology): “[13] models churn in telcom sector as a spread of influence applying the spreading activation [60] method.”
- An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet (2011, background): “2011, Oestricher-Singer and Sundararajan 2010), switching from an existing service provider (Dasgupta et al. 2008), and diffusion of user-generated content in online space (Susarla et al.”
- Churn prediction in new users of Yahoo! answers (2012, background): “Specifically, we would like to identify users that are about to quit, a task known as churn prediction [12, 15, 5, 10, 13, 18, 9].”
- All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement (2015, background): “ositive and negative feedback, which may shed light on why users choose some communities over others. Researchers have also been working on predicting users’ survival (also known as churn prediction) [10, 13, 34] and activity level [11, 37]. They focus on the single-community setting. A number of st”