The social network often contains a large amount of information about users and groups, such as topic evolu-tion mode, group aggregation effect, the law of information dissemination and so on. The mining of these information has become an important task for social network analysis. As one characteristic of the social network, the group aggregation effect is characterized by the community structure of the social network. The discovery of community structure has be-come the basis and key point of other social network analysis tasks. With the rapid growth of the number of online social network users, the traditional community detection methods have been difficult to be used, which contributes to the de-velopment of parallel community detection technology. The current mainstream parallel community detection methods, including Louvain algorithm and label propagation algorithm, were tested in the large-scale data sets, and corresponding advantages and disadvantages were pointed out so as to provide useful information for later applications.%社交网络中往往蕴含着大量用户和群体信息,如话题演化模式、群体聚集效应以及信息传播规律等,对这些信息的挖掘成为社交网络分析的重要任务.社交网络的群体聚集效应作为社交网络的一种特征模式,表现为社交网络的社区结构特性.社区结构的发现已成为其他社交网络分析任务的基础和关键.随着在线社交网络用户数量的急剧增长,传统的社区发现手段已经难以适应,从而催生了并行社区发现技术的发展.对当前主流并行社区发现方法Louvain算法和标签传播算法在超大规模数据集上的可扩展性进行了研究,指出了各自的优缺点,为后续应用提供参考.
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