权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: Inference for Networks: Bridging the Gap between Metric Spaces and Graphs

协作研究：网络推理：弥合度量空间和图之间的差距

基本信息

批准号：
2015298
负责人：
Tianxi Li
金额：
$ 10万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015298&HistoricalAwards=false
关键词：
Collaborative Research Inference Networks Bridging

项目摘要

Network data, representing interactions and relationships between units, has become ubiquitous in many science disciplines and technology areas. Analyzing such complex and structurally novel data requires new ideas and tools beyond the scope of classical statistics. A sequence of methods will be developed for several common statistical analyses involving network data, motivated by various applied problems in cyber-security, social behavior studies, genetics, and medical imaging. These methods can be used to identify the risk factors for the reliability of a complex system, to infer social and peer effects on health-related behaviors, to flexibly model the differential networks between genes, and to infer neuron functionality from brain images. The results will be disseminated through publications and presentations, but will also be incorporated in teaching. The research will include projects suitable for student participation at various levels, and undergraduate research training will be emphasized. The codes will be provided through statistical packages implemented in the programming language R for broader use. The broad theme of the research is developing versatile and flexible network analysis tools by connecting and extending mature statistical methods in metric space to network data. Overall, the technical challenges in developing these tools range from the lack of clear definitions for sampling units and sample sizes, to the discrete and noisy nature of network observations. Addressing such challenges requires extensions and combinations of tools from different research areas, including random matrix theory, optimization algorithms, and statistical inference. Collaborations between the PI and researchers in computer science, social science, and medical sciences will provide opportunities to apply the developed methods to real-world problems in these domains.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.

网络数据代表了单位之间的相互作用和关系，在许多科学学科和技术领域中已经变得无处不在。分析如此复杂和结构新颖的数据需要超越经典统计学范围的新想法和新工具。由于网络安全、社会行为研究、遗传学和医学成像中的各种应用问题，将为涉及网络数据的几种常见统计分析开发一系列方法。这些方法可以用来识别复杂系统可靠性的风险因素，推断社会和同伴对健康相关行为的影响，灵活地对基因之间的差异网络进行建模，以及从大脑图像推断神经元功能。结果将通过出版物和演示文稿传播，但也将纳入教学。研究将包括适合学生参与各级的项目，并将强调本科生的研究培训。这些代码将通过以编程语言R实现的统计程序包提供，以便更广泛地使用。研究的广泛主题是通过将公制空间中的成熟统计方法连接和扩展到网络数据来开发通用和灵活的网络分析工具。总体而言，开发这些工具的技术挑战从缺乏对抽样单位和样本大小的明确定义，到网络观测的离散和噪声性质。应对这些挑战需要来自不同研究领域的工具的扩展和组合，包括随机矩阵理论、优化算法和统计推断。PI与计算机科学、社会科学和医学科学的研究人员之间的合作将提供将开发的方法应用于这些领域的现实世界问题的机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards

混合和延迟奖励下的强化学习增量竞价

DOI：
发表时间：
2022
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Varadaraja, Ashwinkumar;Feng, Zhe;Li, Tianxi;Xu Haifeng
通讯作者：
Xu Haifeng

Link Prediction for Egocentrically Sampled Networks

DOI：
10.1080/10618600.2022.2163648
发表时间：
2018-03
期刊：
Journal of Computational and Graphical Statistics
影响因子：
2.4
作者：
Yun-Jhong Wu;E. Levina;Ji Zhu
通讯作者：
Yun-Jhong Wu;E. Levina;Ji Zhu

Fitting low-rank models on egocentrically sampled partial networks

在以自我为中心采样的部分网络上拟合低秩模型

DOI：
发表时间：
2023
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Chan, Angus G;Li, Tianxi
通讯作者：
Li, Tianxi

Informative core identification in complex networks

复杂网络中的信息核心识别

DOI：
10.1093/jrsssb/qkac009
发表时间：
2023
期刊：
Journal of the Royal Statistical Society Series B: Statistical Methodology
影响因子：
0
作者：
Miao, Ruizhong;Li, Tianxi
通讯作者：
Li, Tianxi

Linear regression and its inference on noisy network‐linked data

DOI：
10.1111/rssb.12554
发表时间：
2020-07
期刊：
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
影响因子：
0
作者：
Can M. Le;Tianxi Li
通讯作者：
Can M. Le;Tianxi Li