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Complex Systems Seminar | Network reconstruction and community detection from dynamics

Tiago Peixoto, Department of Network and Data Science - Central European University
Tuesday, November 5, 2019
11:30 AM-1:00 PM
747 Weiser Hall Map
The observed functional behavior of a wide variety large-scale systems is often the result of a network of pairwise interactions between individual elements. However, in many cases these interactions are hidden from us, either because they are impossible to be measured directly, or because their measurement can be done only at significant experimental cost. In such situations, we are required to infer the network of interactions from the observed functional behavior.

In this talk, I will present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior, that at the same time infers the modular structure (or "communities") present in the network. I will show how 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. I will illustrate the use of the 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.
Building: Weiser Hall
Website:
Event Type: Workshop / Seminar
Tags: Biosciences, Complex Systems, Computational Modeling, Computational Social Science, Computer Science, Natural Sciences, Networks, Research
Source: Happening @ Michigan from The Center for the Study of Complex Systems, The College of Literature, Science, and the Arts, Department of Physics