Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2808797.2808853 | Advances in Social Network Analysis and Mining |
Keywords | DocType | Citations |
community-based prediction,activity change prediction,Skype,online communication,social network,machine learning,local community detection algorithm,global modularity-based method | Conference | 2 |
PageRank | References | Authors |
0.39 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irene Teinemaa | 1 | 29 | 3.99 |
| Anna Leontjeva | 2 | 17 | 2.19 |
| Marlon Dumas | 3 | 25 | 2.54 |
| Riivo Kikas | 4 | 51 | 4.19 |