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Graph Mining Applications to Social Network Analysis

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Managing and Mining Graph Data

Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

The prosperity of Web 2.0 and social media brings about many diverse social networks of unprecedented scales, which present new challenges for more effec- tive graph-mining techniques. In this chapter, we present some graph patterns that are commonly observed in large-scale social networks. As most networks demonstrate strong community structures, one basic task in social network anal- ysis is community detection which uncovers the group membership of actors in a network. We categorize and survey representative graph mining approaches and evaluation strategies for community detection. We then present and discuss some research issues for future exploration.

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Authors and Affiliations

  1. Computer Science & Engineering Arizona State University, Mesa, AZ, USA

    Lei Tang & Huan Liu

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  1. Lei Tang
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  2. Huan Liu
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Correspondence to Lei Tang .

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  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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Tang, L., Liu, H. (2010). Graph Mining Applications to Social Network Analysis. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_16

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_16

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