Inferring Topology of Networks With Hidden Dynamic Variables

Research output: Contribution to journalResearch articleContributedpeer-review



Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering. Most current inference methods rely on time series data recorded from all dynamical variables in the system. In applications, often only some of these time series are accessible, while other units or variables of all units are hidden, i.e. inaccessible or unobserved. For instance, in AC power grids, frequency measurements often are easily available whereas determining the phase relations among the oscillatory units requires much more effort. Here, we propose a network inference method that allows to reconstruct the full network topology even if all units exhibit hidden variables. We illustrate the approach in terms of a basic AC power grid model with two variables per node, the local phase angle and the local instantaneous frequency. Based solely on frequency measurements, we infer the underlying network topology as well as the relative phases that are inaccessible to measurement. The presented method may be enhanced to include systems with more complex coupling functions and additional parameters such as losses in power grid models. These results may thus contribute towards developing and applying novel network inference approaches in engineering, biology and beyond.


Original languageEnglish
Pages (from-to)76682-76692
Number of pages11
JournalIEEE access
Publication statusPublished - 18 Jun 2022


DFG Classification of Subject Areas according to Review Boards

    Subject groups, research areas, subject areas according to Destatis

      Sustainable Development Goals


        • Complex networks, inverse problems, network inference, nonlinear dynamics, power grids