Sparse Bayesian learning for network structure reconstruction based on evolutionary game data

Network structure reconstruction is a fundamental problem for understanding, predicting and controlling the behaviors of complex networked systems and has received growing attention due to the potentials in a wide range of fields. Recent years have witnessed dramatic advances in the field of network structure reconstruction, especially the famous compressed sensing-based methods. However, some neglected disadvantages still exist in the existing works, such as the high measurement correlation existing in the solution matrix, reconstruction behaviors subject to model-based constraints and pure point estimate of the reconstruction results without credibility, which inevitably drag down the reconstruction performance. To address these problems, we propose a new framework of sparse Bayesian learning for network structure reconstruction based on evolutionary game data from the perspective of Bayesian and statistics. Specifically, we formulate the problem of network structure reconstruction as a Bayesian compressed sensing problem. Then, a hierarchical prior model is invoked for conjugated Bayesian inference to obtain the posterior distribution of the reconstructed result, including the reconstructed mean and covariance. Finally, the parameters in the reconstructed results are updated by an iterative estimation procedure. Results from numerical experiments have demonstrated applicability and efficiency of the proposed method and presented superiority over other reconstruction methods.