title: Knotty-centrality: finding the connective core of a complex network.
abstract: A network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph's nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed.
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom. m.shanahan@imperial.ac.uk
authors.forename: Murray
authors.initials: M
authors.lastname: Shanahan
authors.forename: Mark
authors.initials: M
authors.lastname: Wildie
mesh_terms: D000465:Algorithms; D000818:Animals; D002415:Cats; D003071:Cognition / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological