title: Scale-invariant geometric random graphs.
abstract: We introduce and analyze a class of growing geometric random graphs that are invariant under rescaling of space and time. Directed connections between nodes are drawn according to influence zones that depend on node position in space and time, mimicking the heterogeneity and increased specialization found in growing networks. Through calculations and numerical simulations we explore the consequences of scale invariance for geometric random graphs generated this way. Our analysis reveals a dichotomy between scale-free and Poisson distributions of in- and out-degree, the existence of a random number of hub nodes, high clustering, and unusual percolation behavior. These properties are similar to those of empirically observed web graphs.
authors.affiliation: College of Science, National University of Defense Technology, Changsha 410073, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Xie
authors.affiliation: Centre for Networks and Collective Behaviour, Department of Mathematical Sciences, University of Bath, Bath, BA27AY, United Kingdom.
authors.forename: Tim
authors.initials: T
authors.lastname: Rogers