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Modeling and Detecting Community Hierarchies

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Similarity-Based Pattern Recognition (SIMBAD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7953))

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Abstract

Community detection has in recent years emerged as an invaluable tool for describing and quantifying interactions in networks. In this paper we propose a theoretical model that explicitly formalizes both the tight connections within each community and the hierarchical nature of the communities. We further present an efficient algorithm that provably detects all the communities in our model. Experiments demonstrate that our definition successfully models real world communities, and our algorithm compares favorably with existing approaches.

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Author information

Authors and Affiliations

  1. School of Computer Science, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Maria Florina Balcan & Yingyu Liang

Authors
  1. Maria Florina Balcan
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  2. Yingyu Liang
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  2. DAIS, Università Ca’ Foscari, Via Torino 155, 30172, Venice, Italy

    Marcello Pelillo

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Balcan, M.F., Liang, Y. (2013). Modeling and Detecting Community Hierarchies. In: Hancock, E., Pelillo, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2013. Lecture Notes in Computer Science, vol 7953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39140-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-39140-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39139-2

  • Online ISBN: 978-3-642-39140-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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