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A curved exponential family model for complex networks.
Computational and Mathematical Organization Theory ( IF 1.8 ) Pub Date : 2008-11-01 , DOI: 10.1007/s10588-008-9055-x
Mark S Handcock 1 , Martina Morris 1
Affiliation  

Networks are being increasingly used to represent relational data. As the patterns of relations tends to be complex, many probabilistic models have been proposed to capture the structural properties of the process that generated the networks. Two features of network phenomena not captured by the simplest models is the variation in the number of relations individual entities have and the clustering of their relations. In this paper we present a statistical model within the curved exponential family class that can represent both arbitrary degree distributions and an average clustering coefficient. We present two tunable parameterizations of the model and give their interpretation. We also present a Markov Chain Monte Carlo (MCMC) algorithm that can be used to generate networks from this model.

中文翻译:

复杂网络的曲线指数族模型。

网络越来越多地用于表示关系数据。由于关系模式趋于复杂,人们提出了许多概率模型来捕获生成网络的过程的结构特性。最简单的模型无法捕捉到的网络现象的两个特征是各个实体所具有的关系数量的变化及其关系的聚类。在本文中,我们提出了弯曲指数族类内的统计模型,该模型可以表示任意度分布和平均聚类系数。我们提出了模型的两个可调参数化并给出了它们的解释。我们还提出了一种马尔可夫链蒙特卡罗 (MCMC) 算法,可用于从该模型生成网络。
更新日期:2008-11-01
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