Network Time Series: From Dynamics to Coevolution

网络时间序列：从动力学到协同进化

• 批准号: 2113662
• 负责人: Vladas Pipiras
• 金额: $ 22万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113662&HistoricalAwards=false
• 关键词: Network; Time; Series; Dynamics; Coevolution

The last few years have seen a large increase both in the amount of data on real-world networks in numerous research areas and the impact in people’s daily lives. One increasingly important field is in an area called network time series. Some examples include networks that evolve over time (dynamic or temporal networks), or time series over nodes in a network whose dynamics is intricately tied to the underlying network structure; and time series over dynamic networks where the two structures coevolve. Applications specific to this project include social networks with social connections changing owing to social dynamics, vertex specific streams such as text influenced by other vertices, neuroscience with brain functional connectivity networks from fMRI signals and brain structural connectivity networks or sociology and urban planning with migration and economic flows over spatial networks. Despite concerted activity over the last decade, rigorous understanding of network time series models and their applicability in various domains is still challenging owing to the complex emergence of macroscopic structure through microscopic interaction rules between individual network components. The aim of this project is to develop general theoretical foundations for network time series to inform the application of statistical methodology as well as computational techniques in practice whilst being guided by PIs’ collaborations with domain scientists in the areas mentioned above. Additionally, the project will contribute to the training of students with an envisioned data science lab, populated in part by projects from this work providing vertical integration of research experiences. There are three major pillars to this project, arranged sequentially in order of complexity. (1) Network modulated time series. The focus is on multivariate nodal time series with an underlying, possibly latent static network structure. Motivated by recent work on network vector autoregressions, network factor and propagation of chaos models are studied as superior alternatives and extensions. Special cases of the models include opinion dynamics in social networks and network versions of Hodgkin-Huxley and FitzHugh-Nagumo models in neuroscience. Motivated by applications in urban planning, spatial versions of these models will be studied through large network asymptotics. (2) Dynamic networks driven by possibly latent multivariate time series. The PIs will work on their systematic analysis leveraging general time series methods and approaches, especially for discrete-valued time series. Temporal migration and economic flow networks form one targeted area of applications. (3) Co-evolving networks. The PIs study Network models in the scenario where multivariate time series are affected by the underlying network, which itself is affected by the multivariate time series. The PIs will develop mathematical techniques to understand various salient phenomena including the role of self-excitation (the greater the number of times a node interacts with a neighbor the higher the influence this neighbor has in the future including the creation of new connections) and information decay.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.

过去几年，在许多研究领域中，现实世界网络的数据量和对人们日常生活的影响都有了大幅增加。一个日益重要的领域是在一个叫做网络时间序列的领域。一些例子包括随着时间的推移而演变的网络（动态或时间网络），或网络中节点的时间序列，其动态与底层网络结构错综复杂地联系在一起；以及两种结构共同进化的动态网络上的时间序列。该项目的具体应用包括：社会关系因社会动态而变化的社会网络；受其他顶点影响的文本等顶点特定流；从功能磁共振成像信号和大脑结构连接网络中获得脑功能连接网络的神经科学；通过空间网络进行迁移和经济流动的社会学和城市规划。尽管在过去十年中进行了协调一致的活动，但严格理解网络时间序列模型及其在各个领域的适用性仍然具有挑战性，因为单个网络组件之间的微观相互作用规则导致宏观结构的复杂出现。该项目的目的是发展网络时间序列的一般理论基础，以告知统计方法和计算技术在实践中的应用，同时由pi与上述领域的领域科学家合作指导。此外，该项目将有助于培养具有设想的数据科学实验室的学生，部分由本工作的项目提供垂直整合的研究经验。这个项目有三个主要支柱，按照复杂程度顺序排列。(1)网络调制时间序列。重点是多元节点时间序列与潜在的，可能潜在的静态网络结构。受近年来网络向量自回归研究的启发，混沌模型的网络因子和传播作为较好的替代和扩展进行了研究。这些模型的特殊案例包括社交网络中的意见动态和神经科学中霍奇金-赫胥黎模型和菲茨休-南云模型的网络版本。在城市规划应用的激励下，这些模型的空间版本将通过大网络渐近来研究。(2)潜在多元时间序列驱动的动态网络。pi将利用一般的时间序列方法和方法进行系统分析，特别是离散值时间序列。时间迁移和经济流动网络形成了一个目标应用领域。(3)协同进化网络。pi研究的是多变量时间序列受底层网络影响的情况下的网络模型，底层网络本身也受多变量时间序列的影响。pi将开发数学技术来理解各种显著现象，包括自激励的作用（节点与邻居相互作用的次数越多，该邻居在未来的影响就越大，包括新连接的创建）和信息衰减。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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

Learning attribute and homophily measures through random walks

通过随机游走学习属性和同质性度量

• DOI: 10.1007/s41109-023-00558-3
• 发表时间: 2023
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Antunes, Nelson;Banerjee, Sayan;Bhamidi, Shankar;Pipiras, Vladas
• 通讯作者: Pipiras, Vladas

Fluctuation bounds for continuous time branching processes and evolution of growing trees with a change point

连续时间分支过程的波动界限和具有变化点的生长树的演化

• DOI: 10.1214/22-aap1881
• 发表时间: 2023
• 期刊: The Annals of Applied Probability
• 作者: Banerjee, Sayan;Bhamidi, Shankar;Carmichael, Iain
• 通讯作者: Carmichael, Iain

Improved baselines for causal structure learning on interventional data

• DOI: 10.1007/s11222-023-10257-9
• 发表时间: 2023-10-01
• 期刊: STATISTICS AND COMPUTING
• 影响因子: 2.2
• 作者: Richter，Robin;Bhamidi，Shankar;Mukherjee，Sach
• 通讯作者: Mukherjee，Sach

A Conversation with David J. Aldous

与大卫·奥尔德斯的对话

• DOI: 10.1214/22-sts849
• 发表时间: 2022
• 期刊: Statistical Science
• 影响因子: 5.7
• 作者: Bhamidi, Shankar