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arXiv 1202.5587math-phmath.MPmath.PR
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指数随机图模型的一种聚类扩展方法

A cluster expansion approach to exponential random graph models

Mei Yin

摘要:

随机图的指数族是研究最广泛的网络模型之一。我们表明,任何指数随机图模型也可以看作是一个格子气模型与有限的Banach空间范数。然后,该系统可以通过来自统计力学的簇展开方法来处理。特别是,我们推导出收敛的幂级数展开的限制自由能的情况下,小参数。由于自由能是其他随机变量的期望值的生成函数,这表征了该参数区域中极限网络的结构和行为。

英文摘要:

The exponential family of random graphs is among the most widely-studied network models. We show that any exponential random graph model may alternatively be viewed as a lattice gas model with a finite Banach space norm. The system may then be treated by cluster expansion methods from statistical mechanics. In particular, we derive a convergent power series expansion for the limiting free energy in the case of small parameters. Since the free energy is the generating function for the expectations of other random variables, this characterizes the structure and behavior of the limiting network in this parameter region.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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