Collaborative Research: Models for Network Evolution: A Study of Growth and Structure in the Wikipedia

该项目将开发新的网络增长和变化统计模型，并将这些模型应用于研究维基百科的演变。这项研究建立在社会网络的潜在因素模型和高维变量选择和聚类分析的最新进展基础上。利用维基百科条目中的文本及其当前连接结构的信息，研究将估计新条目将出现在哪里，并描述超链接数据集不同区域中的本地图结构。尽管这些模型与维基百科相适应，但其方法论与复杂网络的研究大体相关。维基百科是人类知识的独特镜子，它迅速增长，而且这种增长还在继续。从理解人类如何组织信息的角度来看，重要的是找出维基百科中的“漏洞”，在那里会出现新的条目。同样，人们想知道，比如说，关于亨利八世的信息是否以与同伦理论相同的方式组织。这两种问题都可以使用公开的版本控制数据进行统计分析，这些数据已经存档，以帮助发现维基百科的破坏行为。该研究对人类知识结构的研究有直接影响，对复杂网络变化的研究有间接影响。

The project will develop new statistical models for network growth and change, and apply these to study the evolution of the Wikipedia. The research builds on latent factor models for social networks and recent advances in variable selection and cluster analysis in high dimensions. Using information on the text in Wikipedia entries and its current connectivity structure, the research will estimate where new entries will appear and characterize the local graph structures in different regions of the hyperlinked data set. Although the models are tuned to the Wikipedia, the methodology has general relevance to the study of complex networks.The Wikipedia is a unique mirror of human knowledge It has grown quickly, and this growth continues. From the standpoint of understanding how humans organize information, it is important to identify the "holes" in the Wikipedia, where new entries will arise. Similarly, one wants to know whether information on, say, Henry VIII is organized in the same way as information on Homotopy Theory. Both kinds of questions can be analyzed statistically, using publicly available version control data that has been archived to help discover Wikipedia vandalism. The research has direct impact on the study of the structure of human knowledge, and indirect impact on the study of change in complex networks.