Abstract

The rapid growth of social networking sites enables people to connect to each other more conveniently than ever. With easy-to-use social media, people contribute and consume contents, leading to a new form of human interaction and the emergence of online collective behavior. In this chapter, we aim to understand group structures and properties by extracting and profiling communities in social media. We present some challenges of community detection in social media. A prominent one is that networks in social media are often heterogeneous. We introduce two types of heterogeneity presented in online social networks and elaborate corresponding community detection approaches for each type, respectively. Social media provides not only interaction information but also textual and tag data. This variety of data can be exploited to profile individual groups in understanding group formation and relationships. We also suggest some future work in understanding group structures and properties.

Original languageEnglish (US)
Title of host publicationLink Mining
Subtitle of host publicationModels, Algorithms, and Applications
PublisherSpringer New York
Pages163-185
Number of pages23
Volume9781441965158
ISBN (Electronic)9781441965158
ISBN (Print)9781441965141
DOIs
StatePublished - Jan 1 2010

ASJC Scopus subject areas

  • Medicine(all)

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    Tang, L., & Liu, H. (2010). Understanding group structures and properties in social media. In Link Mining: Models, Algorithms, and Applications (Vol. 9781441965158, pp. 163-185). Springer New York. https://doi.org/10.1007/978-1-4419-6515-8-6