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Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

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  • István A Kovács
  • Robin Palotai
  • Máté S Szalay
  • Peter Csermely

Abstract

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

Suggested Citation

  • István A Kovács & Robin Palotai & Máté S Szalay & Peter Csermely, 2010. "Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0012528
    DOI: 10.1371/journal.pone.0012528
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    References listed on IDEAS

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    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
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    Cited by:

    1. Martin Rosvall & Carl T Bergstrom, 2011. "Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-10, April.
    2. Jordán, Ferenc, 2022. "The network perspective: Vertical connections linking organizational levels," Ecological Modelling, Elsevier, vol. 473(C).
    3. Fu, Xianghua & Liu, Liandong & Wang, Chao, 2013. "Detection of community overlap according to belief propagation and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 941-952.
    4. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
    5. Carlo Piccardi, 2011. "Finding and Testing Network Communities by Lumped Markov Chains," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-13, November.
    6. Zhenping Li & Xiang-Sun Zhang & Rui-Sheng Wang & Hongwei Liu & Shihua Zhang, 2013. "Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.

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