What is it about?

This paper studies the problem of designing estimators (or algorithms) to discover the network of interconnections of interacting units. Take for instance the problem of finding how our brain network is wired by looking at the activity of the neurons.

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Why is it important?

Understanding how complex systems are interconnected can help us uncovering the underlying principles of complexity. Contrary to different strategies in the literature, we developed algorithms to discover the network topology on real time. That it, by looking at real time signals of the units we can estimate who is connected to whom.


The problem of network topology identification has been tackled from many disciplines and different approaches have been formulated. We wanted to provide a formulation from the control theory point of view and make a clear connection to the problem of adaptive observers.

Daniel Burbano Lombana
New York University

Read the Original

This page is a summary of: Discovering the topology of complex networks via adaptive estimators, Chaos An Interdisciplinary Journal of Nonlinear Science, August 2019, American Institute of Physics,
DOI: 10.1063/1.5088657.
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