Followers are not enough: a multifaceted approach to community detection in online social networks.
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.
Department of Mathematics, University of Maryland, College Park, Maryland, United States of America.
David
D
Darmon
LaTTiCe-CNRS, ISC-PIF, Paris, France; Department d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Tarragona, Spain.
Elisa
E
Omodei
Department of Computer Science, University of Colorado, Boulder, Colorado, United States of America.
Joshua
J
Garland
D033062:Friends; D006801:Humans; D020407:Internet; D012111:Residence Characteristics; D061108:Social Media; D060756:Social Networking