CIF: Small: Community Detection in Multilayer Networks with Applications to Functional Connectivity Brain Networks

CIF：小型：多层网络中的社区检测及其在功能连接大脑网络中的应用

• 批准号: 2006800
• 负责人: Selin Aviyente
• 金额: $ 50.65万
• 依托单位: Michigan State University
• 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: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006800&HistoricalAwards=false
• 关键词: CIF; Small; Community; Detection; Multilayer

Networks provide a powerful and compact representation of the internal structure of complex systems consisting of agents that interact with each other. Examples include social networks, the World Wide Web and biological networks of molecules, cells or entire species. Traditional network science assumes that the network nodes are connected by a single edge that captures all interactions between them. However, this is an oversimplification as most real-world networks are built through different types of interactions among the nodes. Examples include social networks, where two individuals can be connected through different types of social ties originating from friendship, collaboration or family relationships; air transportation networks, where different airports can be connected through different airlines; and the brain, where different regions can be interacting across different frequency bands or time points. In recent years, multilayer networks, which incorporate multiple channels of connectivity, have been introduced to model these different modes of communication. A core task in network analysis is to identify and understand communities as they can reveal meaningful structure and provide a better understanding of the overall functioning of networks, such as uncovering functional pathways in metabolic networks, related pages in the World Wide Web or groups of friends in social networks and more. Community detection methods on simple graphs are not sufficient to deal with the complexity of multilayer networks for they cannot leverage the multiple modes of interaction between nodes. This project aims to develop a comprehensive multilayer community detection framework with the help of two complementary approaches, namely heuristic quality function optimization and statistical inference. The connections between these two approaches will be established for multilayer network models with varying degrees of complexity starting with temporal networks going to fully coupled multilayer networks.This project addresses the problem of community detection in multilayer networks through three research thrusts. First, novel normalized-cut based quality functions will be defined for temporal, multiplex and multilayer networks, and computationally efficient algorithms will be developed to optimize these new cost functions. The convergence and consistency of the resulting algorithms will be studied. Next, generalized stochastic block models for temporal, multiplex and multilayer networks will be developed. Connections between maximizing a posteriori probabilities derived from these models and optimizing the heuristic quality functions will be established. Finally, the new community detection methods will be applied to multilayer functional connectivity networks, e.g. temporal and multi-frequency networks, constructed from electroencephalogram (EEG) data to assess well-known task-related networks. This new computational framework for multilayer network community detection can be applied to different types of networks including social, biological and ecological networks; we expect an impact on the fields of brain connectomics and cognitive neuroscience through collaborations with neuroscientists at Michigan State University. As part of the project, a diverse group of interdisciplinary researchers will be trained, and K-12 outreach activities that seek to engage female students will be organized.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.

网络为复杂系统的内部结构提供了强大而紧凑的表示，该结构由相互交互的代理组成。例子包括社交网络，全球网络和分子，细胞或整个物种的生物网络。传统网络科学假设网络节点是通过捕获它们之间所有交互的单个边缘连接的。但是，这是一个过度的简化，因为大多数现实世界网络都是通过节点之间的不同类型的交互构建的。例子包括社交网络，其中两个人可以通过源自友谊，协作或家庭关系的不同类型的社交联系来联系；航空运输网络，可以通过不同的航空公司连接不同的机场；大脑，不同区域可以在不同的频带或时间点上相互作用。近年来，已经引入了包含多个连接性渠道的多层网络来对这些不同的通信模式进行建模。网络分析中的核心任务是识别和理解社区，因为它们可以揭示有意义的结构并更好地理解网络的整体功能，例如在代谢网络中发现功能途径，世界范围的网络中的相关页面或社交网络中的一组朋友等等。简单图表上的社区检测方法不足以处理多层网络的复杂性，因为它们无法利用节点之间的多种交互模式。该项目旨在在两种互补方法的帮助下开发全面的多层社区检测框架，即启发式质量功能优化和统计推断。这两种方法之间的连接将用于多层网络模型，具有不同程度的复杂性，从时间网络开始完全耦合多层网络。该项目通过三个研究推力解决了多层网络中社区检测的问题。首先，将针对时间，多重和多层网络定义新颖的基于标准化的质量功能，并将开发计算有效算法以优化这些新的成本功能。将研究所得算法的收敛性和一致性。接下来，将开发用于时间，多路复用器网络的广义随机块模型。将建立从这些模型得出的后验概率与优化启发式质量功能之间的连接。最后，新的社区检测方法将应用于多层功能连接网络，例如由脑电图（EEG）数据构建的时间和多频网络，以评估众所周知的与任务相关的网络。多层网络社区检测的新计算框架可以应用于不同类型的网络，包括社会，生物学和生态网络；我们期望通过与密歇根州立大学的神经科学家合作，对脑连接组和认知神经科学领域产生影响。作为该项目的一部分，将培训一系列多样化的跨学科研究人员，并将组织试图与女学生吸引的K-12外展活动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。

项目成果

Community Detection in Attributed Networks Using Graph Wavelets

• DOI: 10.1109/ieeeconf56349.2022.10051935
• 发表时间: 2022-10
• 期刊: 2022 56th Asilomar Conference on Signals, Systems, and Computers
• 作者: M. Ortiz-Bouza;Selin Aviyente

scSGL: kernelized signed graph learning for single-cell gene regulatory network inference

• DOI: 10.1093/bioinformatics/btac288
• 发表时间: 2022-05-06
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Karaaslanli, Abdullah;Saha, Satabdi;Maiti, Tapabrata
• 通讯作者: Maiti, Tapabrata

Graph Learning From Noisy and Incomplete Signals on Graphs

从图上的噪声和不完整信号中进行图学习

• DOI: 10.1109/ssp49050.2021.9513838
• 发表时间: 2021
• 期刊: 2021 IEEE Statistical Signal Processing Workshop (SSP
• 作者: Karaaslanli, Abdullah;Aviyente, Selin
• 通讯作者: Aviyente, Selin

Community Detection in Fully-Connected Multi-layer Networks Through Joint Nonnegative Matrix Factorization

• DOI: 10.1109/access.2022.3168659
• 发表时间: 2022-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Al-Sharoa，Esraa M.;Aviyente，Selin
• 通讯作者: Aviyente，Selin

Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection

• DOI: 10.1109/msp.2022.3149471
• 发表时间: 2022-07
• 期刊: IEEE Signal Processing Magazine
• 影响因子: 14.9
• 作者: Selin Aviyente;Abdullah Karaaslanli