title: A novel characterization of amalgamated networks in natural systems.
abstract: Densely-connected networks are prominent among natural systems, exhibiting structural characteristics often optimized for biological function. To reveal such features in highly-connected networks, we introduce a new network characterization determined by a decomposition of network-connectivity into low-rank and sparse components. Based on these components, we discover a new class of networks we define as amalgamated networks, which exhibit large functional groups and dense connectivity. Analyzing recent experimental findings on cerebral cortex, food-web, and gene regulatory networks, we establish the unique importance of amalgamated networks in fostering biologically advantageous properties, including rapid communication among nodes, structural stability under attacks, and separation of network activity into distinct functional modules. We further observe that our network characterization is scalable with network size and connectivity, thereby identifying robust features significant to diverse physical systems, which are typically undetectable by conventional characterizations of connectivity. We expect that studying the amalgamation properties of biological networks may offer new insights into understanding their structure-function relationships.
authors.affiliation: 1] Courant Institute of Mathematical Sciences &Center for Neural Science, New York University [2] NYUAD Institute, New York University Abu Dhabi.
authors.forename: Victor J
authors.initials: VJ
authors.lastname: Barranca
authors.affiliation: Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University.
authors.forename: Douglas
authors.initials: D
authors.lastname: Zhou
authors.affiliation: 1] Courant Institute of Mathematical Sciences &Center for Neural Science, New York University [2] NYUAD Institute, New York University Abu Dhabi [3] Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University.
authors.forename: David
authors.initials: D
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical