PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reconstruction of Causal Networks by Set Covering
Nick Fyson, Tijl De Bie and Nello Cristianini
In: ICANNGA 2011, 14-16 April 2011, Ljubljana, Slovenia.

Abstract

We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local considerations, inferring the neighbourhood of each node in turn. The optimisation problem solved for each individual node can be reduced to a set covering problem, which is known to be NP-hard but can be approximated well in practice. We then extend our approach to account for noisy data, based on the Minimum Description Length principle. We demonstrate our algorithms on synthetic data, generated by an SIR-like epidemiological model.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9058
Deposited By:Nick Fyson
Deposited On:21 February 2012