Change points, memory and epidemic spreading in temporal networks

TL;DR

This paper introduces a unified, data-driven approach to model and detect both short-term memory effects and long-term change points in temporal networks, improving understanding of epidemic spreading dynamics.

Contribution

It develops an arbitrary-order mixed Markov model with change points using a nonparametric Bayesian framework to capture multiple time scales simultaneously.

Findings

Multiscale modeling uncovers significant features hidden in single-scale analyses.

The approach improves epidemic spreading predictions on temporal networks.

Statistically significant features emerge when modeling multiple time scales together.

Abstract

Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. However, most data-driven approaches used to model time-varying networks attempt to capture only a single characteristic time scale in isolation --- typically associated with the short-time memory of a Markov chain or with long-time abrupt changes caused by external or systemic events. Here we propose a unified approach to model both aspects simultaneously, detecting short and long-time behaviors of temporal networks. We do so by developing an arbitrary-order mixed Markov model with change points, and using a nonparametric Bayesian formulation that allows the Markov order and the position of change points to be determined from data without overfitting. In addition, we evaluate the quality of the multiscale model in its capacity to reproduce the spreading of epidemics on the temporal network, and we show that describing multiple time scales simultaneously has a synergistic effect, where statistically significant features are uncovered that otherwise would remain hidden by treating each time scale independently.