摘要
20世纪末复杂网络小世界与无标度特性的发现,使多类复杂系统的结构特性、动力学、决策与控制在21世纪初得到了前所未有的关注与发展.鉴于复杂网络在刻画复杂系统拓扑结构方面的有效性,首先介绍构建具有典型特征静态复杂网络的重要模型与方法,这些模型与方法使复杂网络的构建不再依赖有限且高成本的个体真实交互数据,为多领域研究人员探讨相关科学问题提供了便利条件.其次,随着高精度海量群体交互数据构建采集能力的不断提升,构建随时间演化的动态时序复杂网络成为可能,作为时序网络的一个典型特征,个体交互的时间间隔往往呈现幂律分布,即具有爆发特性,这种爆发特性可显著改变系统中的信息传播、博弈决策过程,鉴于此,总结对真实个体交互数据进行幂律分布定量检验的参数估计方法,介绍泊松过程与排队系统,给出几类时序网络构建的理论与方法.
At the end of the 20th century,the discovery of the characteristics of complex networks,such as small world and scale-freeness,brought unprecedented attention and development in the structural characteristics,dynamics,group games,decision-making,and control of various complex systems in the early 21st century.Given the effectiveness of complex networks in characterizing the underlying topology of complex systems,here we first introduce several representative models and methods for constructing synthetic static complex networks with essential characteristics.Such models and methods change the state of constructing complex networks from individuals’limited and high-cost empirical interaction data,and thus provide an effective solution for constructing static networks for researchers in multiple fields to further explore related scientific issues.In recent years,with the continuous improvement of the ability to collect massive high-precision interaction data of individuals,it is possible to construct temporal networks,which dynamically evolve over time.As an essential feature of temporal networks,the inter-event time of interactions often presents a power-law distribution,namely,the bursty behavior.Existing results have shown that the bursty behavior may significantly change the information dissemination,game,decision-making,and control of temporal networks.We further summarize the parameter estimation method for testing the power-law distribution of empirical individuals’interaction data,and introduce the Poisson process and queuing system,and present typical theories and methods for constructing various temporal networks.
作者
李阿明
侯谷庾
王龙
LI A-ming;HOU Gu-yu;WANG Long(College of Engineering,Peking University,Beijing 100871,China;Institute for Artificial Intelligence,Peking University,Beijing 100871,China;Academy for Advanced Interdisciplinary Studies,Peking University,Beijing 100871,China)
出处
《控制与决策》
EI
CSCD
北大核心
2023年第6期1473-1490,共18页
Control and Decision
基金
国家重点研发计划项目(2022YFA1008400)
国家自然科学基金项目(62036002,62173004)
北京市科技新星项目(Z211100002121105).
关键词
复杂系统
复杂网络
时序网络
幂律分布
无标度特性
爆发特性
complex system
complex network
temporal network
power-law distribution
scale-freeness
bursty behavior
