Dynamic hidden-variable network models

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability connections in the network is then a function these Real-world evolve over time and many exhibit dynamics node characteristics well linking structure. Here we introduce study natural temporal extensions static hidden-variable models with stochastic variables links. controlled by two parameters: one that tunes rate change another at which pairs reevaluate their given current values Snapshots dynamic are equivalent to generated only if link reevaluation sufficiently larger than or an additional mechanism added whereby links actively respond changes Otherwise, out equilibrium respect snapshots structural deviations from models. We examine level persistence considered quantify staticlike behavior. explore versions popular community structure, latent geometry, degree heterogeneity. While do not attempt directly model real networks, comment on interesting qualitative resemblances systems. In particular, speculate some variables, partially explaining presence long-ranged geometrically embedded systems intergroup connectivity modular also discuss possible extensions, generalizations, applications introduced class