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