Empirical Study on Overlapping Community Detection in Question and Answer Sites

Zide Meng 1 Fabien Gandon 1 Catherine Faron Zucker 1 Ge Song 2, 3
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
2 SCALE - Safe Composition of Autonomous applications with Large-SCALE Execution environment
Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués
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.
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Zide Meng, Fabien Gandon, Catherine Faron Zucker, Ge Song. Empirical Study on Overlapping Community Detection in Question and Answer Sites. Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, Aug 2014, Beijing, China. ⟨10.1109/ASONAM.2014.6921608 ⟩. ⟨hal-01075944⟩

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