A Two-Stage Working Model Strategy for Network Analysis Under Hierarchical Exponential Random Graph Models
A Two-Stage Working Model Strategy for Network Analysis Under Hierarchical Exponential Random Graph Models
复制标题
DOI：
10.1109/asonam.2018.8508401
复制
发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Ming Cao;Yong Chen;K. Fujimoto;M. Schweinberger
中科院分区：
文献类型：
--
作者：
Ming Cao;Yong Chen;K. Fujimoto;M. Schweinberger
社交网络数据是复杂的、依赖的数据。在宏观层面上，社会网络通常表现为聚集性，即社会网络由社区组成；在微观层面上，社会网络往往表现出复杂的网络特征，如社区内的传递性。对现实世界的社交网络进行建模需要同时对宏观和微观两个层面进行建模，但现有的许多模型只关注其中一个层面，而忽略了另一个层面。在最近的工作中，[28]引入了一类指数随机图模型(ERGM)来捕捉社区结构以及社区内部的微观特征。虽然有吸引力，但现有的估计具有社区结构的ERGM的方法是不可扩展的。我们提出了一种可扩展的两阶段策略来估计一类具有社区结构的重要的ERGM，它诱导了社区内的传递性。在第一阶段，我们使用一种被称为工作模型的近似模型来估计社区结构。在第二阶段，我们使用带有几何加权的二元和边向共享伙伴项的ERGM来捕捉社区内传递性的精细化形式。我们使用仿真来演示两阶段策略在估计的社区结构方面的性能。此外，我们还证明了在社区内具有几何加权的二向和边向共享伙伴项的估计ERGM在拟合度方面优于工作模型。最后，我们给出了一个高分辨率人类接触网络数据的应用。
Social network data are complex and dependent data. At the macro-level, social networks often exhibit clustering in the sense that social networks consist of communities; and at the micro-level, social networks often exhibit complex network features such as transitivity within communities. Modeling real-world social networks requires modeling both the macro- and micro-level, but many existing models focus on one of them while neglecting the other. In recent work, [28] introduced a class of Exponential Random Graph Models (ERGMs) capturing community structure as well as microlevel features within communities. While attractive, existing approaches to estimating ERGMs with community structure are not scalable. We propose here a scalable two-stage strategy to estimate an important class of ERGMs with community structure, which induces transitivity within communities. At the first stage, we use an approximate model, called working model, to estimate the community structure. At the second stage, we use ERGMs with geometrically weighted dyadwise and edgewise shared partner terms to capture refined forms of transitivity within communities. We use simulations to demonstrate the performance of the two-stage strategy in terms of the estimated community structure. In addition, we show that the estimated ERGMs with geometrically weighted dyadwise and edgewise shared partner terms within communities outperform the working model in terms of goodness-of-fit. Last, but not least, we present an application to high-resolution human contact network data.