摘要
近年来,生成图模型在复杂网络研究中的作用越来越重要。图的生成过程对于研究疾病的蔓延和信息的传播具有重大意义,同时图模型的生成也有助于更深入地研究复杂网络的特性。为了能够生成既符合真实网络特征又具有结构多样性的复杂网络,提出了一种具有社区结构的可调节聚集系数和模块性的无标度网络生成算法——TCMSN(Scale Free Network with Tunable Clustering Coefficient and Modularity)。通过调节混合参数可以调节生成网络的模块性,通过调节社区内连边的概率和混合参数可以对网络聚集系数进行调节。TCMSN采用了合理的连边策略,在不破坏网络结构多样性的情况下,能尽可能维持网络的无标度特性。人工构造数据和真实网络数据的对比实验结果表明,TCMSN算法能够生成可调节聚集系数和模块性的无标度网络模型,且能够生成最接近真实网络社区结构特征的网络模型。
Generating complex network models can help researchers to understand network behaviors and simulate the transmission processes of disease epidemics and information diffusion.It is also important to generate complex networks meeting the characteristics of real networks and having structural diversity.A network generation algorithm TCMSN（Scale-free Network with Tunable Clustering Coefficient and Modularity）was proposed to generate scale-free complex networks with tunable clustering coefficient and modularity.TCMSN can adjust modularity by changing the mixing parameter and adjust clustering coefficient by changing the global preferential attachment probability and mixing parameter of the network.It adopts a reasonable strategy about adding edges in networks to maintain the scale-free characteristics,as much as possible without destroying network diversity.Experimental results on artificial data sets and real networks show that the proposed TCMSN algorithm can not only generate scale-free network model with tunable clustering coefficient and modularity,but also generate network model closed to the community structure of the real networks.
出处
《计算机科学》
CSCD
北大核心
2018年第2期76-83,共8页
Computer Science
基金
国家自然科学基金(61572005)
山西省回国留学人员科研基金(2017-014)资助
关键词
网络生成模型
BA无标度网络
聚集系数
社区结构
Network generation models
BA scale free network
Clustering coefficient
Community structure
