Paper Info
Title
Minimal dynamical structure realisations with application to network reconstruction from data
Abstract
Network reconstruction, i.e., obtaining network structure from data, is a central theme in systems biology, economics, and engineering. Previous work introduced dynamical structure functions as a tool for posing and solving the problem of network reconstruction between measured states. While recovering the network structure between hidden states is not possible since they are not measured, in many situations it is important to estimate the number of hidden states in order to understand the complexity of the network under investigation and help identify potential targets for measurements. Estimating the number of hidden states is also crucial to obtain the simplest state-space model that captures the network structure and is coherent with the measured data. This paper characterises minimal order state-space realisations that are consistent with a given dynamical structure function by exploring properties of dynamical structure functions and developing algorithms to explicitly obtain a minimal reconstruction.
Year
DOI
Venue
2009
10.1109/CDC.2009.5400432
CDC
Keywords
Field
DocType
network reconstruction,state-space methods,network structure,dynamical structure functions,hidden states,state space model,data mining,transfer functions,state space,system biology,probability density function,structure function
Control theory,Computer science,Network simulation,Systems biology,Theoretical computer science,Transfer function,Structure function,Probability density function,State space,Network structure
Conference
ISSN
ISBN
Citations 
0191-2216 E-ISBN : 978-1-4244-3872-3
978-1-4244-3872-3
8
PageRank 
References 
Authors
1.13
2
4
Name
Order
Citations
PageRank
Ye Yuan143861.04
Guy-Bart Vincent Stan2131.65
Sean Warnick319825.76
Jorge M. Goncalves4324.61