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的现象，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。非正式地说，扩散历史重建（RDH）是识别扩散过程的问题，该扩散过程为给定的一组观测提供最佳解释，其中扩散历史是按时间顺序的扩散图。该项目重点关注 RDH 的基础理论和高效、数据驱动的算法。 RDH 的理论和算法可直接应用于流行病学中识别暴露于病毒的人员、网络安全中跟踪计算机病毒/恶意软件的传播以及定位社交网络中泄露的机密信息或谣言的来源和参与者。 主旨 1该项目通过部分观察建立了 RDH 的理论和基本限制，并回答了一些基本问题，例如重建精度和计算复杂性如何随网络规模和数据样本的变化而变化。 Thrust 2 开发了基于深度学习的新算法基础，特别是图神经网络和循环神经网络交叉点的算法基础，并具有部分观察结果。网络拓扑和时间动态被嵌入到细胞或神经元的设计以及神经网络的架构中。 所开发的算法预计在准确性、可扩展性和适用性方面显着超越现有技术。此外，理论和算法是使用合成数据集和真实数据集进行评估的。该项目开发的新深度学习算法及其应用将被整合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群体的学生参与该研究项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。