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Empirical study on overlapping community detection in question and answer sites

机译:问题与答答网站重叠社区检测的实证研究

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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,人们基于他们的兴趣互动。然后,社区检测算法是有用的,可以揭示网络的子结构,并帮助我们找到兴趣组。确定这些社交社区可以为理解和预测用户行为带来好处。但是,对于某种在线社区网站(如问答(Q&A)网站或论坛,没有基于友谊的社交网络结构,这意味着人们不知道他们与谁接触。因此,许多传统的社区检测技术不直接申请。在本文中,我们提出了一种从适合应用群落检测方法的Q&A站点提取数据的经验方法。然后,我们比较我们在从流行的Q&A站点StackOverflow中提取的数据集上应用的三种社区检测方法。我们分析和评论每种方法的结果。

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