Paper Info
Title
Community-centric analysis of user engagement in Skype social network
Abstract
Traditional approaches to user engagement analysis focus on individual users. In this paper we address user engagement analysis at the level of groups of users (social communities). From the entire Skype social network we extract communities by means of representative community detection methods each one providing node partitions having their own peculiarities. We then examine user engagement in the extracted communities putting into evidence clear relations between topological and geographic features of communities and their mean user engagement. In particular we show that user engagement can be to a great extent predicted from such features. Moreover, from the analysis it clearly emerges that the choice of community definition and granularity deeply affect the predictive performance.
Year
DOI
Venue
2015
10.1145/2808797.2809384
Advances in Social Network Analysis and Mining
Keywords
Field
DocType
community-centric analysis,user engagement,Skype social network,social communities,representative community detection method,node partition,topological features,geographic features
Data mining,Semi-supervised learning,Social network,Algorithm design,Computer science,User engagement,Feature extraction,Symmetric matrix,Artificial intelligence,Non-negative matrix factorization,Granularity,Machine learning
Conference
Citations 
PageRank 
References 
3
0.39
12
Authors
6
Name
Order
Citations
PageRank
Giulio Rossetti123521.97
Luca Pappalardo2849.90
Riivo Kikas3514.19
Dino Pedreschi43083244.47
Fosca Giannotti52948253.39
Marlon Dumas6252.54