title: Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.
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.
  
  
  RESULTS
  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 persvasively 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
  The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.
authors.affiliation: Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.
authors.forename: István A
authors.initials: IA
authors.lastname: Kovács
authors.forename: Robin
authors.initials: R
authors.lastname: Palotai
authors.forename: Máté S
authors.initials: MS
authors.lastname: Szalay
authors.forename: Peter
authors.initials: P
authors.lastname: Csermely
mesh_terms: D000465:Algorithms; D019058:Community Networks; D003198:Computer Simulation; D006608:Hierarchy, Social; D006801:Humans; D012111:Residence Characteristics
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