社交网络的离散时间模型
Discrete Temporal Models of Social Networks
摘要：
我们提出了一个家庭的统计模型的社会网络随时间的演变，这是一个扩展的指数随机图模型（ERGMs）。ERGM的许多方法很容易适应这些模型，包括极大似然估计算法。我们讨论这种类型的模型和它们的属性，并给出例子，以及它们用于假设检验和分类的演示。我们相信我们的时间ERG模型代表了一个有用的新框架，用于建模时间演化的社交网络，以及从其他领域（如基因调控电路和通信网络）重新连接网络。
英文摘要：
We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.
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