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Community Detection and Visualization of Networks with the Map Equation Framework

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Measuring Scholarly Impact

Abstract

Large networks contain plentiful information about the organization of a system. The challenge is to extract useful information buried in the structure of myriad nodes and links. Therefore, powerful tools for simplifying and highlighting important structures in networks are essential for comprehending their organization. Such tools are called community-detection methods and they are designed to identify strongly intraconnected modules that often correspond to important functional units. Here we describe one such method, known as the map equation, and its accompanying algorithms for finding, evaluating, and visualizing the modular organization of networks. The map equation framework is very flexible and can identify two-level, multi-level, and overlapping organization in weighted, directed, and multiplex networks with its search algorithm Infomap. Because the map equation framework operates on the flow induced by the links of a network, it naturally captures flow of ideas and citation flow, and is therefore well-suited for analysis of bibliometric networks.

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Notes

  1. 1.

    Collaboration network of network scientists is available for download here: http://mapequation.org/downloads/netscicoauthor2010.net

  2. 2.

    Code for generating significance modules is available for download here: http://www.tp.umu.se/~rosvall/code.html

  3. 3.

    https://bitbucket.org/mapequation/infomap

  4. 4.

    http://www.mingw.org/wiki/GettingStarted

  5. 5.

    http://sourceforge.net/projects/mingwbundle/files/latest/download

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Authors and Affiliations

  1. Integrated Science Lab, Department of Physics, Umeå University, 901 87, Umeå, Sweden

    Ludvig Bohlin, Daniel Edler, Andrea Lancichinetti & Martin Rosvall

Authors
  1. Ludvig Bohlin
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  2. Daniel Edler
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  3. Andrea Lancichinetti
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  4. Martin Rosvall
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Corresponding author

Correspondence to Martin Rosvall .

Editor information

Editors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA

    Ying Ding

  2. University of Antwerp, Antwerp, Belgium

    Ronald Rousseau

  3. University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

    Dietmar Wolfram

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© 2014 Springer International Publishing Switzerland

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Bohlin, L., Edler, D., Lancichinetti, A., Rosvall, M. (2014). Community Detection and Visualization of Networks with the Map Equation Framework. In: Ding, Y., Rousseau, R., Wolfram, D. (eds) Measuring Scholarly Impact. Springer, Cham. https://doi.org/10.1007/978-3-319-10377-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-10377-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10376-1

  • Online ISBN: 978-3-319-10377-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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