An Invariance Principle-Based Concentration Result for Large-Scale Stochastic Pairwise Interaction Network Systems

• 成果类型: Article
• 署名作者: Como, Giacomo; Fagnani, Fabio; Zampieri, Sandro
• 署名单位: Polytechnic University of Turin; Lund University; University of Padua
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3670758
• 发表日期: 2026
• 关键词: evolution; DYNAMICS; LIMITS

摘要

We study stochastic pairwise interaction network systems whereby a finite population of agents, identified with the nodes of a (directed) graph, update their states in response to both individual mutations and pairwise interactions with their neighbors. The considered class of systems includes the main epidemic models-such as the susceptible-infected-susceptible, susceptible-infected-recovered, and susceptible-infected-recovered-susceptible models-certain social dynamics models-such as the voter and antivoter models-as well as evolutionary dynamics on graphs. Since these stochastic systems fall into the class of finite-state Markov chains, they always admit stationary distributions. We analyze the asymptotic behavior of the stationary distributions of stochastic pairwise interaction network systems in the limit as the population size grows large, while the interaction network maintains certain mixing properties. Our approach relies on the use of Lyapunov-type functions to obtain concentration results on these stationary distributions. Notably, our results are not limited to fully mixed population models, as they do apply to a much broader spectrum of interaction network structures, including, e.g., Erd & ouml;s-R & eacute;nyi random graphs.