DocumentCode
116470
Title
Empirical study on overlapping community detection in question and answer sites
Author
Meng, Zhou ; Gandon, Fabien ; Zucker, Catherine Faron ; Ge Song
Author_Institution
INRIA Sophia Antipolis Mediterranee, Sophia Antipolis, France
fYear
2014
fDate
17-20 Aug. 2014
Firstpage
344
Lastpage
348
Abstract
In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites such as question-and-answer (Q&A) sites or forums, there is no friendship based social network structure, which means people are not aware who they are in contact with. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an empirical approach for extracting data from Q&A sites suitable to apply community detection methods. Then we compare three kinds of community detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. We analyze and comment the results of each method.
Keywords
information retrieval; social networking (online); Q&A sites; community detection algorithms; online community sites; overlapping community detection; question and answer sites; social communities; social network structure; Cascading style sheets; Clustering algorithms; Communities; HTML; Layout; Social network services; Vegetation;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on
Conference_Location
Beijing
Type
conf
DOI
10.1109/ASONAM.2014.6921608
Filename
6921608
Link To Document