Dynamic Network Reconstruction from Heterogeneous Datasets
For researchers studying complex systems, this work provides a method to combine multiple experimental datasets for network reconstruction, but it is incremental as it extends existing sparsity techniques.
This paper addresses the reconstruction of dynamic networks from heterogeneous datasets, assuming shared Boolean structure across experiments. It proposes a sampling-based method with group sparsity to integrate multiple datasets, achieving efficient network inference in simulations.
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.