Collaborative Research: III: Small: Reconstruction of Diffusion History in Cyber and Human Networks with Applications in Epidemiology and Cybersecurity

合作研究：III：小：重建网络和人类网络中的扩散历史及其在流行病学和网络安全中的应用

• 批准号: 2324769
• 负责人: Lei Ying
• 金额: $ 30万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324769&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Reconstruction

Diffusion processes in networks can be used to model and study many real-world phenomena, including the spread of information on online social networks, infectious diseases such as COVID-19 in human networks, and computer viruses on the Internet. Informally speaking, reconstruction of diffusion history (RDH) is the problem of identifying a diffusion process that provides the best explanation of a given set of observations, where the diffusion history is a time-sequenced spreading graph. This project focuses on fundamental theories and efficient, data-driven algorithms for RDH. The theories and algorithms for RDH have immediate applications for identifying people exposed to viruses in epidemiology, for tracking the spreading of computer viruses/malware in cyber security, and for locating the sources and participants of leaked classified information or rumors in social networks.Thrust 1 of this project establishes the theories and fundamental limits of RDH with partial observations and answers fundamental questions such as how the reconstruction accuracy and computational complexity scale with network size and data samples. Thrust 2 develops a new algorithmic foundation based on deep learning, especially those at the intersection of graph neural networks and recurrent neural networks, with partial observations. The network topology and temporal dynamics are embedded into the design of cells or neurons and the architecture of the neural networks. The developed algorithms are expected to significantly surpass the state of the art in terms of accuracy, scalability, and applicability. Furthermore, the theories and algorithms are evaluated using both synthetic and real-world datasets. New deep learning algorithms developed under this project and their applications will be integrated into the courses taught by the investigators. The team continues to seek undergraduate students and students from underrepresented groups to involve them in this research project.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.

网络中的扩散过程可用于建模和研究许多现实世界现象，包括在线社交网络上的信息传播，人类网络中的Covid-19等传染病以及互联网上的计算机病毒。从非正式的角度来看，扩散历史记录（RDH）的重建是识别扩散过程的问题，该过程提供了对给定观测值的最佳解释，其中扩散历史记录是时间序列的扩散图。该项目着重于RDH的基本理论和高效，数据驱动的算法。 RDH的理论和算法有立即应用于确定流行病学中暴露于病毒的人，跟踪计算机病毒/恶意软件在网络安全方面的传播，以及在社交网络中找到泄漏的机密信息或谣言的来源和参与者的社交网络中的漏洞和参与者。以及具有网络大小和数据样本的计算复杂度量表。推力2基于深度学习，尤其是图形神经网络和经常性神经网络的相交的新算法基础，并进行部分观察。网络拓扑和时间动力学嵌入到细胞或神经元的设计以及神经网络的架构中。 预计开发的算法将在准确性，可伸缩性和适用性方面显着超过最新技术。此外，使用合成和现实世界数据集评估了理论和算法。根据该项目开发的新的深度学习算法，其应用将集成到研究人员教授的课程中。该团队继续寻找本科生和来自代表性不足的小组的学生，以使他们参与该研究项目。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影响的评估评估的评估来支持的。