A state-space approach to sparse dynamic network reconstruction
For researchers in system identification and network reconstruction, this method offers a more tractable solution for sparse dynamic network reconstruction by reducing parameter complexity and avoiding gradient issues.
This paper presents a state-space method for reconstructing sparse dynamic networks, reducing unknown parameters in model selection and using EM algorithms with Sparse Bayesian Learning to avoid gradient computation difficulties. The approach simplifies model selection and addresses network identifiability.
Dynamic network reconstruction has been shown to be challenging due to the requirements on sparse network structures and network identifiability. The direct parametric method (e.g., using ARX models) requires a large amount of parameters in model selection. Amongst the parametric models, only a restricted class can easily be used to address network sparsity without rendering the optimization problem intractable. To overcome these problems, this paper presents a state-space-based method, which significantly reduces the number of unknown parameters in model selection. Furthermore, we avoid various difficulties arising in gradient computation by using the Expectation Minimization (EM) algorithm instead. To enhance network sparsity, the prior distribution is constructed by using the Sparse Bayesian Learning (SBL) approach in the M-step. To solve the SBL problem, another EM algorithm is embedded, where we impose conditions on network identifiability in each iteration. In a sum, this paper provides a solution to reconstruct dynamic networks that avoids the difficulties inherent to gradient computation and simplifies the model selection.