Random graph models: an overview of modeling approaches
被引:0
|
作者:
Channarond, Antoine
论文数: 0引用数: 0
h-index: 0
机构:
Univ Rouen, UMR6085, Lab Math Salem, F-76821 Mont St Aignan, FranceUniv Rouen, UMR6085, Lab Math Salem, F-76821 Mont St Aignan, France
Channarond, Antoine
[1
]
机构:
[1] Univ Rouen, UMR6085, Lab Math Salem, F-76821 Mont St Aignan, France
来源:
JOURNAL OF THE SFDS
|
2015年
/
156卷
/
03期
关键词:
random graph models;
review;
Erdos-Renyi model;
complex networks;
D O I:
暂无
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
This article nonexhaustively reviews random graph models designed to model interaction networks. It begins with the Erdos-Renyi model. It has been deeply studied, as it is based on simple assumptions: independence and homogeneity of the links, which are however too simplistic for applications. The article then focuses on modeling approaches of the hetereogeneity and of the dependences between the links. It starts from probabilistic models reproducing generative processes of the real-world networks (Barabasi-Albert or Watts-Strogatz models for instance) and arrives to models more suitable for statistics. Exponential models (ERGM or p*) enable to introduce dependences between the desired links. Models with latent variables enable to model heterogeneity of the population and to analyze it.