Statistical Inference for Multilayer Network Data with Applications

多层网络数据的统计推断及其应用

• 批准号: 2014928
• 负责人: Marianna Pensky
• 金额: $ 30万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014928&HistoricalAwards=false
• 关键词: Statistical; Inference; Multilayer; Network; Data

Analysis of stochastic networks is extremely important and is used in a variety of applications such as sociology, biology, genetics, ecology, information technology and national security. Networks are very convenient for describing relationship between nodes that may represent people in a social network or brain regions of a person. One of the properties of the majority of networks is that they can be divided into communities with distinct properties and connection patterns. These partitions can be used for answering a variety of questions such as finding tightly connected social groups, or identifying brain regions' connection patterns associated with a disease. While initial efforts were focused on analysis of a single network model, in the last few years one of the most important directions in network science has shifted to the study of sets of individual networks, the so-called multilayer networks, due to both the versatility of the multilayer networks and the variety of applications that can be addressed using this concept. The objective of this research is to develop tools for theoretical and algorithmic analysis of such networks. The theories developed can be applied to analysis of speech-related brain networks that can be affected by epilepsy surgery. This research will be carried out in collaboration with the Functional Brain Mapping and Brain Computer Interface Lab of Advent Health Hospital for Children. The techniques resulting from this project could be applied in a variety of fields that rely on analysis of multilayer stochastic network data: a) medical practice, since a better understanding of connections between brain regions associated with speech will result in more safe and efficient epileptic treatment options; b) medical research, by providing tools for taking into account individual variations of connections between brain regions associated with particular diseases; c) brain science research, by providing tools for analysis of brain networks and their variations; d) molecular biology, by proposing techniques for analyzing the enzymatic influences between proteins related to various functions; e) statistical genetics, by developing procedures for simultaneous studies of gene networks related to several diseases; f) international relations and finance, by analyzing world trade and financial networks corresponding to various modalities; g) social sciences, by analyzing the similarities and the differences in communities related to various types of social connections. Funding will also be used for training work force by carrying out various educational activities, and promoting interdisciplinary research and diversity.The research agenda of this proposal will substantially advance the fields of non-parametric statistics in general, and the emerging field of network data analysis in particular. The spark of the interest in multilayer networks has led to a stream of publications on the subject. However, these publications fall into two very distinct categories: applications driven papers with no theoretical guarantees of the results and statistical papers where those guarantees are provided, but under very restrictive assumptions. While in many applications the main goal is to determine the differences between communities in different layers or sets of layers, the statistical papers focus entirely on the case where the communities are the same in all layers. Due to the absence of relevant theoretical results, in applications, the authors either utilize ad hoc techniques or are forced to make a questionable assumption that the community structure is the same for all the layers. For this reason, there is an overwhelming need for laying solid theoretical foundations and developing efficient computational algorithms for analysis of multilayer networks with diverse community structures. In particular, the objective of this research is the construction of non-parametric techniques that carry out estimation and clustering of multilayer networks where each layer follows the popular Stochastic Block Model and the community structures coincide for some layers and differ for the others. Furthermore, statistical procedures will be supplemented with the precision guarantees via oracle inequalities and minimax studies. This will be accomplished by application of modern algebraic techniques recently employed by the PI. In summary, the research will significantly broaden the arsenal of methods applicable to analysis of multilayer network data by developing techniques for non-parametric estimation and clustering that require few assumptions, are computationally viable, and are also accompanied by theoretical precision guarantees.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.

随机网络的分析非常重要，在社会学、生物学、遗传学、生态学、信息技术和国家安全等领域都有广泛的应用。网络非常方便地描述节点之间的关系，这些节点可能代表社交网络中的人或人的大脑区域。大多数网络的属性之一是，它们可以被划分为具有不同属性和连接模式的社区。这些分区可以用来回答各种问题，比如寻找紧密联系的社会群体，或者识别与疾病相关的大脑区域的连接模式。虽然最初的努力集中在单个网络模型的分析上，但在过去几年中，网络科学中最重要的方向之一已经转移到对单个网络集的研究上，即所谓的多层网络，这是因为多层网络的多功能性和可以使用该概念处理的各种应用。这项研究的目标是开发用于此类网络的理论和算法分析的工具。开发的理论可以应用于分析与语言相关的大脑网络，这些网络可能受到癫痫手术的影响。这项研究将与安永儿童健康医院的功能脑图和脑机接口实验室合作进行。这个项目产生的技术可以应用于各种领域，这些领域依赖于对多层随机网络数据的分析：a)医学实践，因为更好地了解与语言相关的大脑区域之间的联系将导致更安全和有效的癫痫治疗选择；b)医学研究，通过提供工具来考虑与特定疾病相关的大脑区域之间的联系的个体差异；c)脑科学研究，通过提供分析大脑网络及其变化的工具；d)分子生物学，通过提出分析与各种功能相关的蛋白质之间的酶影响的技术；E)统计遗传学，通过制定同时研究与几种疾病有关的基因网络的程序；f)国际关系和金融，通过分析与各种模式相对应的世界贸易和金融网络；g)社会科学，通过分析与各种社会关系有关的社区的相似性和差异性。这项建议的研究议程将大大推动非参数统计领域，特别是新兴的网络数据分析领域。对多层网络的兴趣的火花导致了关于这一主题的一系列出版物。然而，这些出版物分为两个非常不同的类别：应用程序驱动的论文，没有结果的理论保证；统计论文，其中提供了这些保证，但假设非常有限。虽然在许多应用中，主要目标是确定不同层或不同层组的社区之间的差异，但统计论文完全侧重于所有层的社区都相同的情况。由于缺乏相关的理论结果，在应用中，作者要么使用自组织技术，要么被迫做出一个可疑的假设，即所有层的社区结构都是相同的。因此，迫切需要为具有不同社区结构的多层网络的分析奠定坚实的理论基础和开发高效的计算算法。具体地说，本研究的目标是构建非参数技术来执行多层网络的估计和聚类，其中每一层遵循流行的随机块模型，并且社区结构在某些层重合而在其他层不同。此外，统计程序将通过甲骨文不等和极小极大研究补充精度保证。这将通过应用PI最近采用的现代代数技术来实现。总而言之，这项研究将通过开发非参数估计和聚类技术来显著拓宽适用于多层网络数据分析的方法库，这些技术需要很少的假设，在计算上可行，并伴随着理论上的精度保证。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

Is Clustering Advantageous in Statistical Ill-Posed Linear Inverse Problems?

• DOI: 10.1109/tit.2020.3014409
• 发表时间: 2020-11-01
• 期刊: IEEE TRANSACTIONS ON INFORMATION THEORY
• 影响因子: 2.5
• 作者: Rajapakshage, Rasika;Pensky, Marianna
• 通讯作者: Pensky, Marianna

ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network

ALMA：聚类混合多层网络的交替最小化算法

• 发表时间: 2022
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Xing Fan;Marianna Pensky;Feng Yu;Teng Zhang
• 通讯作者: Teng Zhang

The Hierarchy of Block Models

• DOI: 10.1007/s13171-021-00247-2
• 发表时间: 2020-02
• 期刊: Sankhya A
• 作者: M. Noroozi;M. Pensky

Estimation and clustering in popularity adjusted block model

• DOI: 10.1111/rssb.12410
• 发表时间: 2021-02
• 期刊: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
• 作者: M. Noroozi;R. Rimal;M. Pensky

Discussion of “Confidence Intervals for Nonparametric Empirical Bayes Analysis”

“非参数经验贝叶斯分析的置信区间”的讨论

• DOI: 10.1080/01621459.2022.2096039
• 发表时间: 2022
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Pensky, Marianna