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

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Computational Social Networks

Abstract

Community detection, an important part of network analysis, 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 chapter, 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.

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Authors and Affiliations

  1. Computer Science Department, Galatasaray University, Çırağan Cad. No:36, 34357, Ortaköy/İstanbul, Turkey

    Vincent Labatut

  2. Business Science & Marketing Department, Galatasaray University, Çırağan Cad. No:36, 34357, Ortaköy/İstanbul, Turkey

    Jean-Michel Balasque

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  1. Vincent Labatut
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  2. Jean-Michel Balasque
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Correspondence to Vincent Labatut .

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Editors and Affiliations

  1. Machine Intelligence Research Labs SNIRE, Auburn, Washington, USA

    Ajith Abraham

  2. Faculty of Computers and Information, Information Technology Department, Cairo University, Ahmed Zewal Street 5, Orman, Giza, 12613, Egypt

    Aboul-Ella Hassanien

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Labatut, V., Balasque, JM. (2012). Detection and Interpretation of Communities in Complex Networks: Practical Methods and Application. In: Abraham, A., Hassanien, AE. (eds) Computational Social Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4048-1_4

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  • DOI: https://doi.org/10.1007/978-1-4471-4048-1_4

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