NDLI: Community-Based Prediction of Activity Change in Skype

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Community-Based Prediction of Activity Change in Skype

Content Provider	ACM Digital Library
Author	Leontjeva, Anna Kikas, Riivo Dumas, Marlon Teinemaa, Irene
Abstract	A key problem for facilitators of online communication and social networks is to identify users whose activity is likely to change in the near future. Such predictions may serve as basis for targeted campaigns aimed at sustaining or increasing overall user engagement in the network. A common approach to this problem is to apply machine learning methods to make predictions at the level of individuals. These approaches consider only information about each individual user and, thus, do not exploit the social connections and structure of the network. In this paper, we approach the problem of activity change prediction at the level of communities rather than individuals. We develop predictive models of activity change over communities obtained using state-of-art community detection methods and compare their predictive power with each other and against the single-user baseline and ego networks. The results show that community-level prediction models achieve higher prediction accuracy than the traditional single-user approach, whereas a local community detection algorithm outperforms a global modularity-based method.
Starting Page	73
Ending Page	80
Page Count	8
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2808853
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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