On random network inference using bootstrap

使用 bootstrap 进行随机网络推理

• 批准号: 312350-2013
• 负责人: Gel, Yulia
• 金额: $ 1.34万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=548703
• 关键词: random; network; inference; using; bootstrap

Nowadays, with the explosive growth of online social networking and recent advances on modeling of complex and massive datasets, there exists an ever-rising interest in development of new statistical and computational methods for network analysis. Indeed, while probability models on graphs become increasingly popular to describe a variety of modern disparate social systems and natural phenomena (ranging from media propagation and social communication channels to dynamics of toxic financial assets, outbreaks of infectious diseases and spread of computer viruses), statistical methods for estimation and inference of such random network models are yet limited and substantially undeveloped.

如今，随着在线社交网络的爆炸性增长和复杂海量数据集建模的最新进展，人们对开发新的统计和计算方法进行网络分析的兴趣与日俱增。事实上，尽管图表上的概率模型越来越流行来描述各种现代不同的社会系统和自然现象(从媒体传播和社会交流渠道到有毒金融资产的动态、传染病的爆发和计算机病毒的传播)，但用于估计和推断这种随机网络模型的统计方法仍然有限，而且基本上没有开发出来。