Generating temporal networks with the Ascona model

TL;DR

This paper presents a novel queueing-based framework for generating synthetic continuous-time temporal networks with controllable properties, useful for testing network analysis methods.

Contribution

It introduces a Markovian model for temporal networks using Poisson processes and exponential durations, creating a continuous-time analogue of stochastic block models.

Findings

Generated networks exhibit controllable smoothness and event patterns.

The framework enables validation of community and change-point detection methods.

Extensions include discrete-time and instantaneous-contact models.

Abstract

We introduce a queueing-based sampling framework for continuous-time temporal networks. We focus on a Markovian parametrization in which link start times follow a homogeneous Poisson process and link durations are exponentially distributed. We derive stochastic properties of the resulting link streams and exploit them to generate synthetic temporal networks with controllable smoothness and prescribed event patterns, relevant for the validation and interpretation of methods for community, scale, change-point, and periodicity detection. By coupling this temporal mechanism with block-structured endpoint distributions, we obtain a continuous-time analogue of stochastic block models. We also discuss extensions of the framework, including discrete-time and instantaneous-contact limits.