Reconstruction of Tree Network via Evolutionary Game Data Analysis

As one of the most effective technologies for network reconstruction, compressive sensing can recover signals from a small amount of observed data through sparse search or greedy algorithms in the assumption that the unknown signal is sufficiently sparse on a specific basis. However, there often occurs loss of precision even failure in the process of reconstruction without enough prior information. Therefore, the purpose of this article is to solve the problem of low reconstruction accuracy by mining implicit structural information in the network. Specifically, we propose a novel and efficient algorithm (MCM_TRA) for reconstructing the structure of the K -forked tree network. Based on evolutionary game dynamics, the modified clustering method (MCM) classifies all nodes into two sets, then a two-stage reconstruction algorithm (TRA) is illustrated to recover the node signals in different sets. The experimental results demonstrate that the MCM_TRA enhances the reconstruction accuracy prominently than previous algorithms. Moreover, extensive sensitivity analysis shows that the reconstruction effect can be promoted for a broad range of parameters, which further indicates the superiority of the proposed method.

Structure inference of networked system with the synergy of deep residual network and fully connected layer network

The networked systems are booming in multi-disciplines, including the industrial engineering system, the social system, and so on. The network structure is a prerequisite for the understanding and exploration of networked systems. However, the network structure is always unknown in practice, thus, it is significant yet challenging to investigate the inference of network structure. Although some model-based methods and data-driven methods, such as the phase-space based method and the compressive sensing based method, have investigated the structure inference tasks, they were time-consuming due to the greedy iterative optimization procedure, which makes them difficult to satisfy real-time structure inference requirements. Although the reconstruction time of L1 and other methods is short, the reconstruction accuracy is very low. Inspired by the powerful representation ability and time efficiency for the structure inference with the deep learning framework, a novel synergy method combines the deep residual network and fully connected layer network to solve the network structure inference task efficiently and accurately. This method perfectly solves the problems of long reconstruction time and low accuracy of traditional methods. Moreover, the proposed method can also fulfill the inference task of large scale complex network, which further indicates the scalability of the proposed method.

Complex networks identification using Bayesian model with independent Laplace prior

Identification of complex networks from limited and noise contaminated data is an important yet challenging task, which has attracted researchers from different disciplines recently. In this paper, the underlying feature of a complex network identification problem was analyzed and translated into a sparse linear programming problem. Then, a general framework based on the Bayesian model with independent Laplace prior was proposed to guarantee the sparseness and accuracy of identification results after analyzing influences of different prior distributions. At the same time, a three-stage hierarchical method was designed to resolve the puzzle that the Laplace distribution is not conjugated to the normal distribution. Last, the variational Bayesian was introduced to improve the efficiency of the network reconstruction task. The high accuracy and robust properties of the proposed method were verified by conducting both general synthetic network and real network identification tasks based on the evolutionary game dynamic. Compared with other five classical algorithms, the numerical experiments indicate that the proposed model can outperform these methods in both accuracy and robustness.