Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application

Vincent Labatut () and Jean-Michel Balasque ()
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Vincent Labatut: Bit Lab - GSU - Galatasaray University
Jean-Michel Balasque: GSU - Galatasaray University

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Abstract: Community detection is an important part of network analysis and has become a very popular field of research. This activity resulted in a profusion of community detection algorithms, all different in some not always clearly defined sense. This makes it very difficult to select an appropriate tool when facing the concrete task of having to identify and interpret groups of nodes, relatively to a system of interest. In this article, we tackle this problem in a very practical way, from the user's point of view. We first review community detection algorithms and characterize them in terms of the nature of the communities they detect. We then focus on the methodological tools one can use to analyze the obtained community structure, both in terms of topological features and nodal attributes. To be as concrete as possible, we use a real-world social network to illustrate the application of the presented tools, and give examples of interpretation of their results from a Business Science perspective.

Keywords: Complex Networks; Community detection; Business Science; Community interpretation (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-hpe
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Published in Computational Social Networks: Tools, Perspectives and Applications, Springer, pp.81-113, 2012, ⟨10.1007/978-1-4471-4048-1_4⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00633653

DOI: 10.1007/978-1-4471-4048-1_4

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