Multivariate Analysis for Samples of Networks

网络样本的多变量分析

基本信息

项目摘要

Data collected in the form of networks, often with weighted edges, have become increasingly common in practice, but classical statistical tools do not apply to such data. This project will develop a statistical toolbox of network-aware methods for several common statistical analyses. The main motivating application is neuroimaging, which allows collecting brain connectivity network data from human subjects. These methods can be used to identify brain connectivity patterns associated with disorders, to estimate normal brain development trajectories and deviations from it for children and adolescents, to study changes associated with normal and abnormal aging in older subjects, and to find new subtypes of known disorders. The methods will be developed and extensively tested in close collaboration with neuroscientists.This project develops network analogues for common multivariate analysis tools for vector-valued data, such as estimating the mean, classification, clustering, and principal component analysis. The overarching theme is developing network-aware methods by striking the balance between collapsing the networks to a few global summary measures and treating them as a long single vector of edge weights, with the goal to obtain methods that are not only accurate, but also scientifically interpretable. The technical challenges in developing such methods arise, broadly speaking, from the need to impose high-level network structure, such as communities, onto low-level network features, such as individual edge weights. Addressing these challenges will involve sophisticated tools from modern random matrix theory, structured penalties, advanced optimization techniques, and computationally efficient algorithms, to be developed for each of the new network analysis tools proposed. The close collaboration with neuroscientists and psychiatrists will ensure that the statistical tools developed are thoroughly tested and vetted in the neuroimaging community. Project results will be widely disseminated through publications, presentations, and software packages, in both statistical and neuroimaging venues, and are expected to raise the current standards for statistical network analysis in the field of brain connectomics. The project will also train graduate students in an important emerging area of modern statistics and help them develop advanced computing and interdisciplinary collaboration skills.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.
以网络形式收集的数据,往往带有加权边,在实践中越来越普遍,但传统的统计工具不适用于这类数据。该项目将为若干常见统计分析开发一个网络感知方法统计工具箱。 主要的激励应用是神经成像,它允许从人类受试者收集大脑连接网络数据。 这些方法可用于识别与疾病相关的大脑连接模式,估计儿童和青少年的正常大脑发育轨迹及其偏差,研究老年受试者与正常和异常衰老相关的变化,并发现已知疾病的新亚型。 这些方法将与神经科学家密切合作开发和广泛测试。该项目为向量值数据的常见多变量分析工具开发网络模拟,例如估计均值,分类,聚类和主成分分析。总体主题是通过在将网络分解为几个全局摘要度量和将它们视为一个长的单一边缘权重向量之间取得平衡来开发网络感知方法,目标是获得不仅准确而且可科学解释的方法。 广义上讲,开发这种方法的技术挑战来自于需要将高级网络结构(例如社区)强加到低级网络特征(例如个体边权重)上。 应对这些挑战将涉及现代随机矩阵理论,结构化惩罚,先进的优化技术和计算效率高的算法,将开发用于每一个新的网络分析工具的复杂工具。 与神经科学家和精神病学家的密切合作将确保开发的统计工具在神经成像领域得到彻底的测试和审查。 项目成果将通过出版物、演讲和软件包在统计和神经成像领域广泛传播,预计将提高脑连接组学领域统计网络分析的现行标准。 该项目还将在现代统计学的一个重要新兴领域培训研究生,并帮助他们发展先进的计算和跨学科协作技能。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值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(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
High-dimensional Gaussian graphical model for network-linked data
  • DOI:
  • 发表时间:
    2019-07
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tianxi Li;Cheng Qian;E. Levina;Ji Zhu
  • 通讯作者:
    Tianxi Li;Cheng Qian;E. Levina;Ji Zhu
Recovering shared structure from multiple networks with unknown edge distributions
  • DOI:
  • 发表时间:
    2019-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Keith D. Levin;A. Lodhia;E. Levina
  • 通讯作者:
    Keith D. Levin;A. Lodhia;E. Levina
Latent space models for multiplex networks with shared structure
具有共享结构的多重网络的潜在空间模型
  • DOI:
    10.1093/biomet/asab058
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    MacDonald, P W;Levina, E;Zhu, J
  • 通讯作者:
    Zhu, J
GRAPH-AWARE MODELING OF BRAIN CONNECTIVITY NETWORKS
  • DOI:
    10.1214/22-aoas1709
  • 发表时间:
    2023-09-01
  • 期刊:
  • 影响因子:
    1.8
  • 作者:
    Kim,Yura;Kessler,Daniel;Levina,Elizaveta
  • 通讯作者:
    Levina,Elizaveta
Overlapping Community Detection in Networks via Sparse Spectral Decomposition
通过稀疏谱分解进行网络中的重叠社区检测
  • DOI:
    10.1007/s13171-021-00245-4
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Arroyo, Jesús;Levina, Elizaveta
  • 通讯作者:
    Levina, Elizaveta
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Elizaveta Levina其他文献

Elizaveta Levina的其他文献

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{{ truncateString('Elizaveta Levina', 18)}}的其他基金

FRG: Collaborative Research: Flexible Network Inference
FRG:协作研究:灵活的网络推理
  • 批准号:
    2052918
  • 财政年份:
    2021
  • 资助金额:
    $ 25万
  • 项目类别:
    Standard Grant
RTG: Understanding dynamic big data with complex structure
RTG:理解结构复杂的动态大数据
  • 批准号:
    1646108
  • 财政年份:
    2017
  • 资助金额:
    $ 25万
  • 项目类别:
    Continuing Grant
Conference proposal: From Industrial Statistics to Data Science
会议提案:从工业统计到数据科学
  • 批准号:
    1542123
  • 财政年份:
    2015
  • 资助金额:
    $ 25万
  • 项目类别:
    Standard Grant
Statistical Tools for Analyzing Multiple Networks
用于分析多个网络的统计工具
  • 批准号:
    1521551
  • 财政年份:
    2015
  • 资助金额:
    $ 25万
  • 项目类别:
    Standard Grant
FRG: Collaborative Research: Unified statistical theory for the analysis and discovery of complex networks
FRG:协作研究:用于分析和发现复杂网络的统一统计理论
  • 批准号:
    1159005
  • 财政年份:
    2012
  • 资助金额:
    $ 25万
  • 项目类别:
    Standard Grant
Statistical Methods for Network Data
网络数据的统计方法
  • 批准号:
    1106772
  • 财政年份:
    2011
  • 资助金额:
    $ 25万
  • 项目类别:
    Continuing Grant
Discovering Sparse Covariance Structures in High Dimensions
发现高维稀疏协方差结构
  • 批准号:
    0805798
  • 财政年份:
    2008
  • 资助金额:
    $ 25万
  • 项目类别:
    Continuing Grant
Exploiting Special Structures in High-Dimensional Data Classification
在高维数据分类中利用特殊结构
  • 批准号:
    0505424
  • 财政年份:
    2005
  • 资助金额:
    $ 25万
  • 项目类别:
    Standard Grant

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