Random graph models for dynamic networks

TL;DR

This paper introduces dynamic network models based on continuous-time Markov processes, enabling analysis of evolving networks and inference of properties like community structure and evolution rates from temporal data.

Contribution

It generalizes standard static network models to dynamic cases with efficient algorithms for data fitting and inference.

Findings

Effective algorithms for fitting models to temporal network data

Ability to estimate network evolution time constants

Inference of community structures from dynamic data

Abstract

We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.