Replication data for: Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-group Blog Citation Dynamics in the 2004 US Presidential Election

Methods for analysis of network dynamics have seen great progress in the past decade. This paper shows how current methods of Dynamic Network logistic- Regression (DNR; a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US Presidential Election cycle. This is a particularly interesting case because it marks the de- but of Internet-based media such as blogs and social networking websites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all DNC/RNC-designated blog-citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.网络动力学分析方法在过去十年间取得了长足进展。本文阐述了如何利用当前的动态网络逻辑回归（Dynamic Network Logistic Regression, DNR；时间指数随机图模型（Temporal Exponential Random Graph Models）的特例）方法，在面板数据（panel data）情境下构建网络动力学的决策理论模型。本文同时提供了用于模型构建与评估的实用启发式方法。我们通过将这些技术应用于2004年美国总统选举周期内采样得到的动态博客网络，展示了其应用效能。该案例极具研究价值，因其标志着博客、社交网站这类互联网媒体正式成为美国政治版图中获得制度认可的组成部分。通过采用所有经民主党全国委员会（Democratic National Committee, DNC）与共和党全国委员会（Republican National Committee, RNC）认定的博客引用网络的纵向样本，我们得以检验各类策略性、制度性及平衡理论机制，以及季节性、政治事件等外生因素随时间推移对博客间相互引用倾向的影响。结合基于偏差的模型选择准则与基于模拟的模型适配性检验方法，我们确定了最能刻画竞争博客选择行为的过程组合。