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Formal Concept Analysis of Social Networks pp 41–58Cite as

Descriptive Community Detection

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Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Subgroup discovery and community detection are standard approaches for identifying (cohesive) subgroups. This paper presents an organized picture of recent research in descriptive community (and subgroup) detection. Here, it summarizes approaches for the identification of descriptive patterns targeting both static and dynamic (sequential) relations. We specifically focus on attributed graphs, i.e.,complex relational graphs that are annotated with additional information. This relates to attribute information, for example, assigned to the nodes and/or edges of the graph. Combining subgroup discovery and community detection, we also summarize an efficient and effective approach for descriptive community detection.

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Notes

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Authors and Affiliations

  1. Tilburg Center for Cognition and Communication (TiCC), Tilburg University, Tilburg, Netherlands

    Martin Atzmueller

  2. Research Center for Information System Design (ITeG), University of Kassel, Kassel, Germany

    Martin Atzmueller

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  1. Martin Atzmueller
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Correspondence to Martin Atzmueller .

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Editors and Affiliations

  1. Department of Computer Science & Engineering, University of Quebec in Outaouais, Quebec, Canada

    Rokia Missaoui

  2. Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  3. Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia

    Sergei Obiedkov

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Atzmueller, M. (2017). Descriptive Community Detection. In: Missaoui, R., Kuznetsov, S., Obiedkov, S. (eds) Formal Concept Analysis of Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-64167-6_3

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