Change points, memory and epidemic spreading in temporal networks: a new scientific paper

Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. Though recent advances in the study of network systems have been moving beyond the more traditional approach of considering them as static or growing entities, and instead have been introducing more realistic descriptions that allow them to change arbitrarily in time, most data-driven approaches used to model time-varying networks attempt to capture only a single characteristic time scale in isolation.

In “Change points, memory and epidemic spreading in temporal networks”, a new paper out in Nature Scientific Reports, ISI Research Leader Laetitia Gauvin and ISI Researcher Tiago Peixoto propose a unified approach to model both aspects simultaneously, detecting short and long-time behaviors of temporal networks.

The scientists developed an arbitrary-order mixed Markov model with change points, then they evaluated the quality of the multiscale model in its capacity to reproduce the spreading of epidemics on the temporal network. Results show that the most plausible models tend to mix both short-time memory and many change points, and those tend to capture well the nontrivial epidemic behavior observed in the original data. Furthermore, the inferred models with change points typically uncover higher-order memory than the simpler stationary variants, demonstrating that the mixed approach is more powerful than considering individual ones in isolation.

“Change points, memory and epidemic spreading in temporal networks”, Laetitia Gauvin, Tiago Peixoto. Nature Scientific Reports, 19th October 2018. Link:https://www.nature.com/articles/s41598-018-33313-1