Statistical properties of random clique networks

• Published in: Frontiers of physics Vol. 12; no. 5; pp. 251 - 257
• Main Authors: Ding, Yi-Min, Meng, Jun, Fan, Jing-Fang, Ye, Fang-Fu, Chen, Xiao-Song
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.10.2017; Springer Nature B.V
• Subjects: Astronomy; Astrophysics and Cosmology; Atomic; Clustering; Coefficients; communicability; complex networks; Condensed Matter Physics; Modular structures; Molecular; motifs; Optical and Plasma Physics; Particle and Nuclear Physics; Physics; Physics and Astronomy; random clique networks; Research Article; Social networks; Theoretical physics; 人工神经网络; 复杂网络; 模块化结构; 统计性质; 网络模型; 聚类系数; 蛋白质相互作用网络; 随机; random clique networks; complex networks; motifs; communicability
• ISSN: 2095-0462; 2095-0470
• DOI: 10.1007/s11467-017-0682-x

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erdös and Rényi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.