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Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks

机译：追随者还不够：在线社交网络中社区检测的多方面方法

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摘要

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.
机译：在在线社交媒体网络中，个人通常具有数百甚至数千个连接，这些连接不仅将这些用户链接到朋友，同事和同事，而且还链接到新闻媒体，名人和组织。在这些复杂的社交网络中，根据所研究网络的属性，社交网络文献中所研究的“社区”可能具有截然不同的含义。考虑到这些网络的多面性，我们认为在线社交网络中的社区检测也应该是多方面的，以捕获“社区”的所有不同且有价值的观点。在本文中，我们着重于三种类型的社区基于关注者的结构性社区：基于活动，主题和交互的社区。我们使用结构网络的三种不同权重分析Twitter数据集，以突出显示这三种社区类型，然后推断与这些权重相关联的社区。我们显示，通过研究这四种社区类型何时以及如何产生相似以及完全不同的社区结构，可以获得有关社交网络中复杂社区结构的有趣见解。

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