On random network inference using bootstrap

如今，随着在线社交网络的爆炸式增长和复杂海量数据集建模的最新进展，人们对开发新的网络分析统计和计算方法的兴趣日益浓厚。事实上，虽然图形概率模型越来越流行，用于描述各种现代不同的社会系统和自然现象（从媒体传播和社会沟通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，而且基本上尚未开发。

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.