EAGER: Interactive Visualization and Modeling of Latent Communities

EAGER：潜在社区的交互式可视化和建模

• 批准号: 1138094
• 负责人: Peter Brusilovsky
• 金额: $ 14.01万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1138094&HistoricalAwards=false
• 关键词: EAGER; Interactive; Visualization; Modeling; Latent

In many areas of professional and social life, people form communities based on some obvious feature, so that members tend to be similar in some aspect of interest or behavior. However, as the number of groups expands, new dimensions of similarity are introduced. This project will focus on the ways to understand many different communities that differ in many dimensions and to present that understanding in ways that help to understand the community structure. The modern web provides a number of alternative data sources for discover communities, with activities ranging from blog comments to tagging of resources. This data can be used to discover and model communities reliably and present the complex data in clear and efficient ways that decision makers and other stakeholders can readily understand. The visualization of the complex data will also be interactive, permitting exploration of the data. The knowledge of the existence of these communities can be used to support recommendation approaches, such as identification of mentors or development of wide scientific collaborations.

在职业和社会生活的许多领域，人们根据一些明显的特征形成社区，使成员在兴趣或行为的某些方面趋于相似。然而，随着群体数量的扩大，引入了新的相似性维度。 这个项目将侧重于如何理解许多不同的社区，在许多方面不同，并提出这种理解的方式，有助于了解社区结构。 现代网络为发现社区提供了许多替代数据源，活动范围从博客评论到资源标记。 这些数据可用于可靠地发现和建模社区，并以决策者和其他利益相关者易于理解的清晰有效的方式呈现复杂的数据。复杂数据的可视化也将是交互式的，允许对数据进行探索。这些社区存在的知识可以用来支持推荐方法，如确定导师或发展广泛的科学合作。