Sampling and Inference for Large Networks

大型网络的采样和推理

• 批准号: RGPIN-2017-05480
• 负责人: Johnson, Brad
• 金额: $ 1.02万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=654719
• 关键词: Sampling; Inference; Large; Networks

In the current landscape of "Big Data", large networks (graphs) are pervasive objects and have attracted much interest. A (social) network is a complex relational graph consisting of nodes (actors) and edges (relations), where both nodes and edges may have a number of attributes (covariates) associated with them. Examples include social networks such as Facebook, citation and collaboration networks (such as arXiv), web graphs (such as Wikipedia) and communication networks. Researchers are interested in modelling network structures and relations, as well as how these depend on the node and edge attributes; and, possibly, how they evolve over time. The investigation and analysis of these large networks can prove difficult due to the sheer number of nodes and edges and associated attribute data. Another difficulty that researchers are faced with is the prospect of having only a single observed network from which estimates are obtained. The general objectives for this research are to investigate efficient sampling methods on networks and to investigate both parametric and nonparametric inference for network models based on samples using both frequentist and Bayesian methods.***Specific program objectives include research on specific sampling techniques, such as ranked based sampling techniques and resampling techniques, for making inferences about large networks when analyzing the whole network is not computationally feasible. Through this research, I plan to train at least three M.Sc. students and two Ph.D. students as well as fixe undergraduate students.

在当前的“大数据”格局中，大型网络（图）是普遍存在的对象，并引起了人们的广泛兴趣。 （社交）网络是由节点（参与者）和边（关系）组成的复杂关系图，其中节点和边都可能具有与其关联的多个属性（协变量）。 示例包括 Facebook 等社交网络、引用和协作网络（如 arXiv）、网络图（如维基百科）和通信网络。 研究人员对网络结构和关系建模以及它们如何依赖于节点和边缘属性感兴趣；以及它们如何随着时间的推移而演变。 由于节点和边以及相关属性数据的数量庞大，对这些大型网络的调查和分析可能会很困难。 研究人员面临的另一个困难是只能通过一个观察网络来获得估计值。 本研究的总体目标是研究网络上的有效采样方法，并使用频率论和贝叶斯方法研究基于样本的网络模型的参数和非参数推理。***具体计划目标包括研究特定采样技术，例如基于排序的采样技术和重采样技术，以便在分析整个网络在计算上不可行时对大型网络进行推理。 通过这项研究，我计划培养至少三名硕士学位。学生和两名博士。学生以及固定本科生。