基于稀疏贝叶斯算法的演化博弈网络重构

演化博弈是自然和社会系统中一种常见的互动类型，探知演化博弈网络的拓扑结构是理解其功能和集体行为的基础。对于演化博弈网络，个体的博弈行为通常难以用动力学方程进行描述，而且相关的时序信息一般数量有限并且是离散的，因此在有限的个体博弈信息下重构网络的结构有着重要的研究意义。本文基于稀疏贝叶斯学习方法进一步发展了演化博弈网络的重构方法，通过在随机网络和小世界网络上的数值模拟验证该方法的有效性。与先前的基于L1范数的方法相比，该方法同样能够在较少的个体博弈信息下实现网络的重构，并且具有更高的重构效率和更强的噪声鲁棒性。

Reconfiguration of Evolutionary Game Network Based on Sparse Bayesian Algorithm

Evolutionary game is a common type of interaction model in natural and social systems. Exploring the topological structure of an evolutionary game network is the basis for understanding its functions and collective behaviors. For evolutionary game networks, the individual game behavior is usually difficult to be described by dynamic equations, and the related time series information is generally limited and discrete, so it is important to reconstruct the network structure under the limited individual game information. This paper develops the reconstruction method of evolutionary game network based on the sparse Bayesian learning method. The validity of this method is verified by numerical simulation on random networks and small-world networks. Compared with previous L1 norm-based methods, this method can also reconstruct networks with less individual game information, and has higher reconstructing efficiency and stronger noise robustness.