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2017 | OriginalPaper | Chapter

Labeled DBN Learning with Community Structure Knowledge

Authors : E. Auclair, N. Peyrard, R. Sabbadin

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

Learning interactions between dynamical processes is a widespread but difficult problem in ecological or human sciences. Unlike in other domains (bioinformatics, for example), data is often scarce, but expert knowledge is available. We consider the case where knowledge is about a limited number of interactions that drive the processes dynamics, and on a community structure in the interaction network. We propose an original framework, based on Dynamic Bayesian Networks with labeled-edge structure and parsimonious parameterization, and a Stochastic Block Model prior, to integrate this knowledge. Then we propose a restoration-estimation algorithm, based on 0-1 Linear Programing, that improves network learning when these two types of expert knowledge are available. The approach is illustrated on a problem of ecological interaction network learning.

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Appendix
Available only for authorised users
Footnotes
1
Here and in the following, upper case letters are used for random variables, and lower case letters for a realization.
 
2
Trophic levels are represented in Fig. 1, right: \(TL(1)=0\), \(TL(2)=1\), \(TL(3)=TL(4)=2\).
 
3
\(P_{\mathcal {LG}_\rightarrow , \theta }(x^0)\) will not be estimated.
 
4
Known variables were selected uniformly at random.
 
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Metadata
Title
Labeled DBN Learning with Community Structure Knowledge
Authors
E. Auclair
N. Peyrard
R. Sabbadin
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71245-1

Electronic ISBN: 978-3-319-71246-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71246-8_10

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