Eur. Phys. J. B (2013) 86: 298
https://doi.org/10.1140/epjb/e2013-30986-5

Regular Article

Derivative-variable correlation reveals the structure of dynamical networks

¹ Faculty of Information Studies in Novo mesto, 8000 Novo mesto, Slovenia
² School of Advanced Social Studies in Nova Gorica, 5000 Nova Gorica, Slovenia

^a e-mail: zoran.levnajic@fis.unm.si

Received: 28 October 2012
Received in final form: 8 February 2013
Published online: 1 July 2013

Abstract

We propose a conceptually novel method of reconstructing the topology of dynamical networks. By examining the correlation between the variable of one node and the derivative of another node’s variable, we derive a simple matrix equation yielding the network adjacency matrix. Our assumptions are the possession of time series describing the network dynamics, and the precise knowledge of the internal interaction functions. Our method involves a tunable parameter, allowing for the reconstruction precision to be optimized within the constraints of given dynamical data. The method is illustrated on a simple example, and the dependence of the reconstruction precision on the dynamical properties of time series is discussed. Our theory is in principle applicable to any weighted or directed network whose interaction functions are known.