title: Network quotients: structural skeletons of complex systems.
abstract: A defining feature of many large empirical networks is their intrinsic complexity. However, many networks also contain a large degree of structural repetition. An immediate question then arises: can we characterize essential network complexity while excluding structural redundancy? In this article we utilize inherent network symmetry to collapse all redundant information from a network, resulting in a coarse graining which we show to carry the essential structural information of the "parent" network. In the context of algebraic combinatorics, this coarse-graining is known as the "quotient." We systematically explore the theoretical properties of network quotients and summarize key statistics of a variety of "real-world" quotients with respect to those of their parent networks. In particular, we find that quotients can be substantially smaller than their parent networks yet typically preserve various key functional properties such as complexity (heterogeneity and hub vertices) and communication (diameter and mean geodesic distance), suggesting that quotients constitute the essential structural skeletons of their parent networks. We summarize with a discussion of potential uses of quotients in analysis of biological regulatory networks and ways in which using quotients can reduce the computational complexity of network algorithms.
authors.affiliation: Department of Computing and Information Technology, Fudan University, Shanghai 200433, People's Republic of China.
authors.forename: Yanghua
authors.initials: Y
authors.lastname: Xiao
authors.forename: Ben D
authors.initials: BD
authors.lastname: MacArthur
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001703:Biophysics / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009415:Nerve Net; D016571:Neural Networks, Computer