Complex Network Modeling With Power-Law Activating Patterns and Its Evolutionary Dynamics
该研究提出一个考虑个体以幂律速率在激活与静息状态间随机切换的复杂网络模型，利用马尔可夫链和更新理论分析网络激活规模的平稳分布，并研究两人两策略演化博弈动力学，揭示无突变同质网络中囚徒困境的关键合作条件。
Complex network theory provides a unifying framework for the study of structured dynamic systems. The current literature emphasizes a widely reported phenomenon of intermittent interaction among network vertices. In this article, we introduce a complex network model that considers the stochastic switching of individuals between activated and quiescent states at power-law rates and the corresponding evolutionary dynamics. By using the Markov chain and renewal theory, we discover a homogeneous stationary distribution of activated sizes in the network with power-law activating patterns and infer some statistical characteristics. To better understand the effect of power-law activating patterns, we study the two-person-two-strategy evolutionary game dynamics, demonstrate the absorbability of strategies, and obtain the critical cooperation conditions for prisoner’s dilemmas in homogeneous networks without mutation. The evolutionary dynamics in real networks are also discussed. Our results provide a new perspective to analyze and understand social physics in time-evolving network systems.