Probabilistic Constraint Programming for Parameters Optimisation of Generative Models

TL;DR

This paper introduces a probabilistic constraint programming approach to optimize parameters of generative models for complex networks, demonstrated on brain network reconstruction, offering improved parameter space characterization and reduced computational costs.

Contribution

It presents a novel application of probabilistic constraint programming for parameter optimization in generative network models, outperforming existing methods in efficiency and accuracy.

Findings

Better characterization of parameter space

Lower computational cost compared to other methods

Effective reconstruction of brain networks

Abstract

Complex networks theory has commonly been used for modelling and understanding the interactions taking place between the elements composing complex systems. More recently, the use of generative models has gained momentum, as they allow identifying which forces and mechanisms are responsible for the appearance of given structural properties. In spite of this interest, several problems remain open, one of the most important being the design of robust mechanisms for finding the optimal parameters of a generative model, given a set of real networks. In this contribution, we address this problem by means of Probabilistic Constraint Programming. By using as an example the reconstruction of networks representing brain dynamics, we show how this approach is superior to other solutions, in that it allows a better characterisation of the parameters space, while requiring a significantly lower computational cost.