title: Taxonomies of networks from community structure.
abstract: The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.|Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA.|Department of Physics, University of Oxford, Oxford OX1 3PU, UK.|CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Jukka-Pekka
authors.initials: JP
authors.lastname: Onnela
authors.affiliation: Mathematical and Computational Finance Group, University of Oxford, Oxford OX1 3LB, UK.|CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Fenn
authors.affiliation: Department of Physics, University of Oxford, Oxford OX1 3PU, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Reid
authors.affiliation: Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, OX1 3LB, UK.|CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Mason A
authors.initials: MA
authors.lastname: Porter
authors.affiliation: Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics and Institute for Advanced Materials, Nanoscience & Technology, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Mucha
authors.affiliation: Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.|CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Mark D
authors.initials: MD
authors.lastname: Fricker
authors.affiliation: Department of Physics, University of Oxford, Oxford OX1 3PU, UK.|Oxford Centre for Integrative Systems Biology, Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK.|CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Nick S
authors.initials: NS
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical