Paper Info
Title
Automated community detection on social networks: useful? efficient? asking the users
Abstract
In most online social networks, with the increasing number of users and content, the problem of contact filtering becomes more and more present. The recent introduction of such features in online social networks -- for instance, Circles in Google+ or Facebook Smart lists -- shows that it is a problem they are confronted to. In this paper, we explore this question through multidisciplinary aspects. First, we discuss about this issue of groups management in the context of social networks. Then, we present several techniques from the state of the art to automatically find meaningful groups of contacts in a user's contact list. Finally, we asked Facebook users to evaluate these solutions on their own Facebook network, both to compare the solutions among themselves and to assess how pertinent the best ones are according to them. The conclusions of this study is that a network analysis approach can strongly improve the efficiency of an automated detection of groups on networks, which could be used, combined with profile data extraction, to design intelligent management of groups of contacts.
Year
DOI
Venue
2012
10.1145/2189736.2189745
WI&C
Keywords
Field
DocType
own facebook network,network analysis approach,social network,online social network,intelligent management,automated detection,facebook user,groups management,automated community detection,contact list,facebook smart list,network analysis,question answering,machine learning
Data science,World Wide Web,Knowledge validation,Social network,Question answering,Multidisciplinary approach,Computer science,Filter (signal processing),Data extraction,Network analysis,Intelligent management
Conference
Citations 
PageRank 
References 
5
0.44
5
Authors
3
Name
Order
Citations
PageRank
Cazabet Remy11319.71
Maud Leguistin250.44
Frédéric Amblard343051.43