Varying-coefficient models for dynamic networks
Varying-coefficient models for dynamic networks
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DOI：
10.1016/j.csda.2020.107052
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
Jihui Lee;Gen Li;James D. Wilson
中科院分区：
文献类型：
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作者：
Jihui Lee;Gen Li;James D. Wilson
动态网络通常用于对随时间观察的关系数据进行建模。此类数据的统计模型应捕获关系系统的时间变化以及每个网络内的结构依赖性。因此，有效地对动态网络进行推理是一项计算上具有挑战性的任务，并且即使对于中等规模的系统，许多模型也很棘手。鉴于这些挑战，提出了一系列称为变系数指数随机图模型（VCERGM）的动态网络模型，通过平滑变化的参数来表征网络拓扑的演化。 VCERGM 提供了一个可解释的动态网络模型，可以推断动态网络中的时间异质性。 VCERGM 的估计是通过最大伪似然技术实现的，从而为复杂动态网络的统计推断提供了计算上易于处理的策略。此外，提出了一个引导假设检验框架，用于测试观察到的动态网络序列的时间异质性。对美国参议院共同投票网络的应用和综合模拟研究都表明，VCERGM 提供了相关且可解释的模式，并且比现有方法具有显着优势。
Dynamic networks are commonly used to model relational data that are observed over time. Statistical models for such data should capture both the temporal variation of the relational system as well as the structural dependencies within each network. As a consequence, effectively making inference on dynamic networks is a computationally challenging task, and many models are intractable even for moderately sized systems. In light of these challenges, a family of dynamic network models known as varying-coefficient exponential random graph models (VCERGMs) is proposed to characterize the evolution of network topology through smoothly varying parameters. The VCERGM provides an interpretable dynamic network model that enables the inference of temporal heterogeneity in dynamic networks. Estimation of the VCERGM is achieved via maximum pseudo-likelihood techniques, thereby providing a computationally tractable strategy for statistical inference of complex dynamic networks. Furthermore, a bootstrap hypothesis testing framework is presented for testing the temporal heterogeneity of an observed dynamic network sequence. Application to the U.S. Senate co-voting network and comprehensive simulation studies both reveal that the VCERGM provides relevant and interpretable patterns and has significant advantages over existing methods.