Dynamic Network Reconstruction from Heterogeneous Datasets

TL;DR

This paper introduces efficient methods for reconstructing dynamic networks from multiple heterogeneous datasets, leveraging group sparsity and Bayesian techniques to ensure consistent network structure inference across experiments.

Contribution

It proposes a novel sampling-based approach combined with extended l1 and Bayesian methods for structured group sparsity in dynamic network reconstruction.

Findings

Methods outperform traditional approaches in numerical simulations.

Structured sparsity ensures consistent network inference across datasets.

Practical guidelines for applying these techniques in real-world scenarios.

Abstract

Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations to parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that underlying networks share the same Boolean structure across all experiments. Parametric models for dynamical structure functions are derived to describe causal interactions between measured variables. Multiple datasets are integrated into one regression problem with additional demands of group sparsity to assure network sparsity and structure consistency. To acquire structured group sparsity, we propose a sampling-based method, together with extended versions of l1 methods and sparse Bayesian learning. The performance of the proposed methods is benchmarked in numerical simulation. In summary, this paper presents efficient methods on network reconstruction from multiple experiments, and reveals practical experience that could guide applications.