title: Simple, distance-dependent formulation of the Watts-Strogatz model for directed and undirected small-world networks.
abstract: Small-world networks-complex networks characterized by a combination of high clustering and short path lengths-are widely studied using the paradigmatic model of Watts and Strogatz (WS). Although the WS model is already quite minimal and intuitive, we describe an alternative formulation of the WS model in terms of a distance-dependent probability of connection that further simplifies, both practically and theoretically, the generation of directed and undirected WS-type small-world networks. In addition to highlighting an essential feature of the WS model that has previously been overlooked, namely the equivalence to a simple distance-dependent model, this alternative formulation makes it possible to derive exact expressions for quantities such as the degree and motif distributions and global clustering coefficient for both directed and undirected networks in terms of model parameters.
authors.affiliation: Center for Neural Science, New York University, New York, New York 10003, USA.
authors.forename: H Francis
authors.initials: HF
authors.lastname: Song
authors.affiliation: Center for Neural Science, New York University, New York, New York 10003, USA and NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China.
authors.forename: Xiao-Jing
authors.initials: XJ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical