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

该项目将为网络增长和变化开发新的统计模型，并将其应用于研究维基百科的演变。 该研究建立在社交网络的潜在因素模型和最近的进展，变量选择和聚类分析在高维。 利用维基百科条目中的文本信息及其当前的连接结构，该研究将估计新条目将出现的位置，并描述超链接数据集不同区域的局部图结构。 虽然这些模型是根据维基百科调整的，但这种方法与复杂网络的研究具有普遍的相关性。维基百科是人类知识的一面独特的镜子，它发展迅速，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，重要的是要识别维基百科中的“漏洞”，新条目将出现。 同样，人们想知道关于亨利八世的信息是否与同伦论的信息以同样的方式组织。 这两种问题都可以通过统计学方法进行分析，使用公开的版本控制数据，这些数据已经存档，以帮助发现维基百科的破坏行为。 它的研究直接影响到人类知识结构的研究，间接影响到复杂网络变化的研究。

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 Both kinds of questions can be analyzed statistically, using publicly available version control data that has been archived to help discover Wikipedia vandalism.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.