Graphische Darstellung der Anpassungsgüte akteursbasierter Netzwerkentwicklungsmodelle

基于参与者的网络开发模型拟合优度的图形表示

• 批准号: 24614601
• 负责人: Professor Dr. Ulrik Brandes
• 金额: --
• 依托单位: Departement Geistes-, Sozial- und Staatswissenschaften (GESS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2006
• 资助国家: 德国
• 起止时间: 2005-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/24614601?language=en
• 关键词: Graphische; Darstellung; der; Anpassungsg; te

A large number of graphical methods have been proposed for the diagnosis of statistical models. The,primary motivation is that such models are often quite restrictive in their assumptions, and it is difficult to assess in which aspects there is an important deviation between an empirical data set and a statistical model supposed to match this data.Graphical methods are present to the human eye features of the data, and features of the datamodel combination, that are not easily discerned by other means and that may point to aspects of the data that are not well represented by the model. The simultaneous graphical exploration of model, data, and fit may lead to finding and fitting a better model.In social network analysis, graphical representations have been of paramount importance from the very beginning, since the complexity of a network structure can often be brought out much more clearly in a visualization than by algebraic properties or numerical descriptive parameters. On the other hand, there are no readily available methods for visualizing evolving networks so as to support reliable model evaluation.The aim of this project is to develop and employ network visualization methods for the interpretation and model diagnosis of statistical models for social network evolution. The models of Snijders [28, and related publications listed in the overall project proposal], which form the basis of our ECRP proposal, are rather algebraic and require complementary graphical methods to be utilized more easily. These methods will be developed in a feedback-loop with both the core modeling project and our substantively and empirically oriented partner projects.

已经提出了大量的图形方法用于统计模型的诊断。主要的动机是，这样的模型往往是相当限制在他们的假设，这是很难评估在哪些方面有一个重要的偏差之间的经验数据集和统计模型应该匹配这个数据。图形方法是目前的人眼功能的数据，和功能的datamodel组合，这些信息不容易通过其他方式识别，并且可能指向模型没有很好地表示的数据方面。在社会网络分析中，图形化表示从一开始就至关重要，因为网络结构的复杂性往往可以在可视化中比代数属性或数值描述参数更清楚地显示出来。另一方面，有没有现成的方法来可视化不断发展的网络，以支持可靠的模型evaluation.The本项目的目的是开发和采用网络可视化方法的解释和模型诊断的统计模型的社会网络的演变。Snijders的模型[28，以及整个项目提案中列出的相关出版物]构成了我们ECRP提案的基础，它们相当代数，需要补充的图形方法才能更容易地使用。这些方法将与核心建模项目和我们的实质性和经验导向的合作伙伴项目一起在反馈循环中开发。