Abstract: 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.

Key words: evolutionary game network, sparse Bayes, network reconstruction