Empirical study on overlapping community detection in question and answer sites

Zide Meng, Fabien L. Gandon, C. Faron-Zucker, Ge Song
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引用次数: 10

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.
问答网站重叠社区检测的实证研究
在许多社交网络中,人们根据自己的兴趣进行互动。然后,社区检测算法对揭示网络的子结构并帮助我们找到感兴趣的群体很有用。识别这些社交社区有助于理解和预测用户行为。然而,对于某些在线社区网站,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接触的是谁。因此,许多传统的社区检测技术并不直接适用。在本文中,我们提出了一种从问答网站中提取适合应用社区检测方法的数据的经验方法。然后,我们比较了我们在热门问答网站StackOverflow提取的数据集上应用的三种社区检测方法。我们对每种方法的结果进行了分析和评论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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