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Empirical study on overlapping community detection in question and answer sites

机译:问答站点重叠社区发现的实证研究

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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.
机译:在许多社交网络中,人们根据自己的兴趣进行交互。然后,社区检测算法可用于揭示网络的子结构并帮助我们找到兴趣小组。识别这些社交社区可以为理解和预测用户行为带来好处。但是,对于诸如问与答(Q&A)网站或论坛之类的某些在线社区网站,没有基于友谊的社交网络结构,这意味着人们不知道自己与谁联系。因此,许多传统的社区检测技术不能直接应用。在本文中,我们提出了一种从Q&A站点提取数据的经验方法,适用于应用社区检测方法。然后,我们比较了三种社区检测方法,这些方法适用于从受欢迎的问答网站StackOverflow提取的数据集。我们分析并评论每种方法的结果。

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