论文标题
基于以持续性信息为中心数据的动态网络建模
Modeling of Dynamic Networks based on Egocentric Data with Durational Information
论文作者
论文摘要
动态网络的建模 - 随着时间的流逝而发展的网络 - 在许多领域都具有多种应用程序。特别是在流行病学中，需要对人类性关系网络进行数据驱动的建模，以建模和模拟性传播疾病的传播。但是，关于此类网络的动态网络数据非常困难，但是，在一个时间点上，网络的egeCentrional采样数据更容易获得，并提供了一些有关受访者性行为的服务。 Krivitsky and Handcock（2014）提出了一个可分离的时间ERGM（Stergm）框架，该框架促进了TIE持续时间分布的可分离建模和TIE形成的结构动力学。在这项工作中，我们通过研究Stergm过程的长期特性，开发将STERGM拟合到中心采样数据的方法，并扩展Krivitsky，Handcock和Morris（2011）将网络尺寸调整方法扩展到动态模型。
Modeling of dynamic networks -- networks that evolve over time -- has manifold applications in many fields. In epidemiology in particular, there is a need for data-driven modeling of human sexual relationship networks for the purpose of modeling and simulation of the spread of sexually transmitted disease. Dynamic network data about such networks are extremely difficult to collect, however, and much more readily available are egocentrically sampled data of a network at a single time point, with some attendant information about the sexual history of respondents. Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM) framework, which facilitates separable modeling of the tie duration distributions and the structural dynamics of tie formation. In this work, we apply this modeling framework to this problem, by studying the long-run properties of STERGM processes, developing methods for fitting STERGMs to egocentrically sampled data, and extending the network size adjustment method of Krivitsky, Handcock, and Morris (2011) to dynamic models.
