CAREER: Model-based Analysis of Dynamic Networks using Continuous-time Network Models

职业：使用连续时间网络模型对动态网络进行基于模型的分析

• 批准号: 2047955
• 负责人: Kevin Xu
• 金额: $ 55万
• 依托单位: University of Toledo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047955&HistoricalAwards=false
• 关键词: CAREER; Model; based; Analysis; Dynamic

Networks are all around us in many forms, ranging from online social networks to public transportation networks to gene networks in biology. Most networks change over time and are often called temporal or dynamic networks. In this project, a framework for modeling and analyzing dynamic networks that change continuously over time will be developed, even though the networks may only be periodically observed. This framework advances the interdisciplinary field of network science along with the computer and information sciences by developing models to separate the underlying dynamics of the networks from the times at which the networks are observed. The framework can be applied to analyze dynamic network data in many scientific disciplines and in public health applications, including networks of face-to-face interactions between people, which can help scientists better understand the spread of infectious diseases such as COVID-19. This project advances education in network science by creating a curriculum for instruction of dynamic networks at the undergraduate and graduate levels. The project also trains new graduate and undergraduate students, including female students from the University of Toledo's ACM-W chapter, in interdisciplinary data science research. Finally, the project develops and integrates methods for analyzing dynamic networks into the open-source DyNetworkX Python package to reach others who could use them in impactful ways.Temporal dynamics in networks are known to provide crucial information about the underlying complex systems being modeled by the networks. While significant advances have been made towards understanding the structure of static networks, dynamics are usually incorporated in an ad-hoc manner by creating discrete time snapshots aggregated over some arbitrary time period, primarily for convenience of analysis. The goal of this project is to develop a unified framework for model-based analysis of dynamic networks using continuous-time models that can be applied to both discrete- and continuous-time dynamic network data. Towards this goal, the research team will target five specific aims: 1) learning continuous-time network models from aggregated counts of relational events over time, 2) creating Hawkes process-based generative models for timestamped events with durations, 3) developing kernel smoothing approaches for analyzing dynamic networks, 4) modeling different types of measurement error in dynamic network data, and 5) creating time- and memory-efficient dynamic graph data structures to enable analysis of large dynamic networks with high temporal resolution. Dynamics of networks are given minimal coverage in current network science curricula and textbooks. The model-based analysis techniques to be developed in this project build upon fundamental network theory and empirical observations about real networks and are thus ideal for integration into a typical graduate or undergraduate network science course. The investigator will develop a publicly-available curriculum for instruction on dynamic network representations, models, and analysis methods. The results of this project will provide a glimpse of the possibilities enabled by continuous-time network models and guide future research and education efforts on dynamic networks.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.

网络以多种形式在我们周围，从在线社交网络到公共交通网络再到生物学的基因网络。大多数网络会随着时间而变化，通常称为时间或动态网络。在这个项目中，即使只能定期观察到网络，也将开发一个用于建模和分析动态网络的框架。该框架通过开发模型将网络的基本动力学与观察到网络的时代分开的模型来推动网络科学的跨学科领域以及计算机和信息科学。该框架可用于分析许多科学学科和公共卫生应用程序中的动态网络数据，包括人们之间面对面互动的网络，这可以帮助科学家更好地了解Covid-19等传染病的传播。该项目通过在本科和研究生级别创建一个动态网络的课程来推动网络科学的教育。该项目还培训了跨学科数据科学研究的新研究生和本科生，包括托莱多大学ACM-W分会的女学生。最后，该项目开发并集成了将动态网络分析到开放源代码Dynetworkx Python软件包中的方法，以吸引其他可以以有影响力的方式使用它们的人。网络中的暂时性动态已知可以提供有关由由由该系统建模的基础复杂系统的重要信息网络。尽管已经在理解静态网络的结构方面取得了重大进展，但通常通过在某个任意时间段内汇总的离散时间快照来纳入动态，主要是为了方便分析。该项目的目的是使用连续时间模型开发一个统一的框架，以基于模型的动态网络分析，这些模型可以应用于离散时间和连续时间动态网络数据。为了实现这一目标，研究团队将针对五个具体目标：1）从关系事件的汇总计数中学习连续的时间网络模型，2）创建基于霍克斯过程的生成模型，用于使用持续时间的时间戳事件，3）开发内核平滑分析动态网络的方法，4）对动态网络数据中不同类型的测量误差进行建模，以及5）创建时间和内存有效的动态图数据结构，以启用具有高时间分辨率的大型动态网络。在当前的网络科学课程和教科书中，网络的动态范围最少。基于模型的分析技术将基于基本网络理论和关于真实网络的经验观察，因此非常适合将其集成到典型的毕业生或本科网络科学课程中。研究人员将开发一个公共可用的课程，以指导动态网络表示，模型和分析方法。该项目的结果将瞥见通过连续的时间网络模型实现的可能性，并指导对动态网络的未来研究和教育工作。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评估来支持的。和更广泛的影响审查标准。

项目成果

A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks

• DOI: 10.48550/arxiv.2205.09263
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Zhipeng Huang;Hadeel Soliman;Subhadeep Paul;Kevin S. Xu

Analyzing escalations in militarized interstate disputes using motifs in temporal networks

使用时间网络中的主题分析军事化国家间争端的升级

• DOI: 10.1007/978-3-030-93409-5_44
• 发表时间: 2022
• 期刊: Proceedings of the 10th International Conference on Complex Networks and Their Applications
• 作者: Do, Hung N.;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

A hybrid adjacency and time-based data structure for analysis of temporal networks

用于分析时态网络的混合邻接和基于时间的数据结构

• DOI: 10.1007/978-3-030-93409-5_49
• 发表时间: 2022
• 期刊: Proceedings of the 10th International Conference on Complex Networks and Their Applications
• 作者: Hilsabeck, Tanner;Arastuie, Makan;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

Counteracting filter bubbles with homophily-aware link recommendations

通过同质感知链接推荐来消除过滤气泡

• DOI: 10.1007/978-3-031-17114-7_15
• 发表时间: 2022
• 期刊: Lecture notes in computer science
• 作者: Warton, Robert;Volny, Chris;Xu, Kevin S.
• 通讯作者: Xu, Kevin S.

A hybrid adjacency and time-based data structure for analysis of temporal networks

• DOI: 10.1007/s41109-022-00489-5
• 发表时间: 2022-06
• 期刊: Applied Network Science
• 影响因子: 2.2
• 作者: Tanner Hilsabeck;Makan Arastuie;Kevin S. Xu