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: 2324770
• 负责人: Hanghang Tong
• 金额: $ 30万
• 依托单位: University of Illinois at Urbana-Champaign
• 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=2324770&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，重建扩散历史（RDH）是识别扩散过程的问题，该扩散过程提供了对给定观测集的最佳解释，其中扩散历史是时间序列扩展图。该项目侧重于RDH的基础理论和高效的数据驱动算法。RDH的理论和算法可以立即应用于识别流行病学中暴露于病毒的人，跟踪网络安全中计算机病毒/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。该项目的第一个目标是通过部分观察建立RDH的理论和基本限制，并回答基本问题，例如重建精度和计算复杂度随网络大小和数据样本而变化。Thrust 2开发了一个基于深度学习的新算法基础，特别是那些处于图神经网络和递归神经网络交叉点的算法，并具有部分观察。网络拓扑结构和时间动力学被嵌入到细胞或神经元的设计和神经网络的体系结构中。 开发的算法预计将显着超过最先进的准确性，可扩展性和适用性方面。此外，理论和算法进行评估，使用合成和真实世界的数据集。在该项目下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。该团队继续寻求本科生和来自代表性不足群体的学生参与这项研究项目。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。

项目成果

Learning Node Abnormality with Weak Supervision

• DOI: 10.1145/3583780.3614950
• 发表时间: 2023-10
• 期刊: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
• 作者: Qinghai Zhou;Kaize Ding;Huan Liu;H. Tong

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

• DOI: 10.1145/3583780.3615170
• 发表时间: 2022-06
• 期刊: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
• 作者: Boxin Du;Changhe Yuan;Fei Wang;Hanghang Tong