Inferring the Past on Markovian Models of Networks

根据马尔可夫网络模型推断过去

基本信息

批准号：
2113671
负责人：
Min Xu
金额：
$ 20万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113671&HistoricalAwards=false
关键词：
Inferring Past Markovian Models Networks

项目摘要

A major challenge in statistics and data science is the analysis of network data. Network data describe interactions and relationships between individual entities. The most prominent example is social network data, but other important examples include internet hyperlink networks, protein interaction networks, air route networks between cities, and disease transmission networks between people. These interaction networks generally start with a few individuals and, as time goes on, they attract, infect, or recruit more members and create more interactions. The goal of this project is to develop probabilistic models that accurately describe the growth process of real-world networks and to use these models to extract important information from large scale network data. Algorithms and software packages will be developed that enable users to answer questions such as, which individuals were the earliest members of a social network, or does the network contain one growing community or multiple? The results of this project will have applications in public health, social science, computer science, and national security. The project also provides research training opportunities for graduate students. The framework developed by the PI models a random network as a combination of a preferential attachment (PA) tree and Erdos-Renyi (ER) random edges. The PA tree describes the growth process of a network and may be regarded as the signal and the ER random edges can be interpreted as the noise. This framework includes many existing network models as special cases and allows practitioners to trade-off model complexity and computational complexity. Scalable methodology based on Gibbs sampling will be developed to tackle inference problems such as constructing confidence sets for the root nodes or inferring the community membership of the nodes of a network. Theoretical analysis, based on existing probabilistic properties of preferential attachment models, will also be conducted to assess the quality of statistical inference as a function of the signal-to-noise ratio and to understand the information limits of these problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

统计和数据科学的主要挑战是网络数据的分析。网络数据描述了各个实体之间的互动和关系。最突出的例子是社交网络数据，但其他重要的例子包括Internet超链接网络，蛋白质相互作用网络，城市之间的空中路线网络以及人们之间的疾病传播网络。这些相互作用网络通常从几个人开始，随着时间的流逝，它们吸引，感染或招募更多成员并创建更多的互动。该项目的目的是开发概率模型，以准确描述现实世界网络的增长过程，并使用这些模型从大型网络数据中提取重要信息。将开发算法和软件包，使用户能够回答诸如社交网络中最早的个人，或者网络中包含一个不断增长的社区还是多个问题？该项目的结果将在公共卫生，社会科学，计算机科学和国家安全方面提供应用。该项目还为研究生提供了研究培训机会。 PI开发的框架将随机网络建模为优先附件（PA）树和Erdos-Renyi（ER）随机边缘的组合。 PA树描述了网络的生长过程，可以被视为信号，并且ER随机边缘可以解释为噪声。该框架包括许多现有网络模型作为特殊情况，并允许从业人员权衡模型的复杂性和计算复杂性。将开发基于Gibbs采样的可扩展方法来解决推理问题，例如为根节点构建置信度或推断网络节点的社区成员资格。理论分析基于优先依恋模型的现有概率特性，还将进行评估统计推断的质量作为信噪比的函数，并了解这些问题的信息限制。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛的criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia rection the Appliation。