Community Detection and Visualization of Networks with the Map Equation Framework

  • Ludvig Bohlin
  • Daniel Edler
  • Andrea Lancichinetti
  • Martin RosvallEmail author


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.


Random Walker Community Detection Module Assignment Huffman Code Network Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ludvig Bohlin
    • 1
  • Daniel Edler
    • 1
  • Andrea Lancichinetti
    • 1
  • Martin Rosvall
    • 1
    Email author
  1. 1.Integrated Science Lab, Department of PhysicsUmeå UniversityUmeåSweden

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