基于纵向二部关系数据的影响网络研究
Inferring Influence Networks from Longitudinal Bipartite Relational Data
摘要：
纵向二分关系数据表征了成对参与者之间关系的演变，其中参与者具有两种不同的类型，并且关系仅存在于不同类型之间。一个共同的目标是了解时间依赖性，特别是哪些演员关系煽动后来的演员关系。目前有两种解决这一问题的方法。第一种方法将每个时间段的二分数据投影到一个单分网络中，并使用现有的单分网络模型。不幸的是，在计算投影和生成模型的网络通过这个过程中获得的信息是稀缺的。第二种方法使用两个单分的\emph{influence networks}表示依赖关系，对应于两个参与者类型。采用这种方法的现有模型在影响网络中是双线性的，这在计算和解释方面带来了挑战。我们提出了一种新的生成模型，允许估计加权，定向影响网络，并没有遭受这些缺点。所提出的模型在影响网络中是线性的，允许使用现成的软件工具进行推断。我们证明了我们的估计是一致的情况下的模型误设定和几乎渐近等价的双线性估计。我们展示了所提出的模型在模拟研究和每周的国际国家互动分析的性能。
英文摘要：
Longitudinal bipartite relational data characterize the evolution of relations between pairs of actors, where actors are of two distinct types and relations exist only between disparate types. A common goal is to understand the temporal dependencies, specifically which actor relations incite later actor relations. There are two existing approaches to this problem. The first approach projects the bipartite data in each time period to a unipartite network and uses existing unipartite network models. Unfortunately, information is lost in calculating the projection and generative models for networks obtained through this process are scarce. The second approach represents dependencies using two unipartite \emph{influence networks}, corresponding to the two actor types. Existing models taking this approach are bilinear in the influence networks, creating challenges in computation and interpretation. We propose a novel generative model that permits estimation of weighted, directed influence networks and does not suffer from these shortcomings. The proposed model is linear in the influence networks, permitting inference using off-the-shelf software tools. We prove our estimator is consistent under cases of model misspecification and nearly asymptotically equivalent to the bilinear estimator. We demonstrate the performance of the proposed model in simulation studies and an analysis of weekly international state interactions.
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