Network reconstruction and community detection from dynamics
This method addresses network inference challenges in fields like epidemiology and physics, but it appears incremental as it builds on existing nonparametric Bayesian approaches.
The authors tackled the problem of jointly reconstructing networks and detecting communities from observed functional dynamics, achieving a synergistic effect where edge correlations improved reconstruction accuracy and community detection simultaneously.
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.