title: How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection.
abstract: By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not asO(2n), withnbeing the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.
authors.affiliation: Laboratoire d'Informatique de Paris 6 , Université Pierre et Marie Curie , Paris, France.
authors.forename: Binh-Minh
authors.initials: BM
authors.lastname: Bui-Xuan
authors.affiliation: Department of Mathematics , Imperial College London , Queen's Gate, London, UK.
authors.forename: Nick S
authors.initials: NS
authors.lastname: Jones
keywords: community structure; network analysis; parametrized complexity