NDLI: Derivative-variable correlation reveals the structure of dynamical networks

Content Provider	Springer Nature Link
Author	Levnajić, Zoran
Copyright Year	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.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISSN	14346028
Journal	The European Physical Journal B
Volume Number	86
Issue Number	7
e-ISSN	14346036
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-07-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistical and Nonlinear Physics Condensed Matter Physics Physics Statistical Physics, Dynamical Systems and Complexity Fluid- and Aerodynamics Solid State Physics
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Condensed Matter Physics

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