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

网络中的扩散过程可用于模拟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，人际网络中的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，该扩散过程提供了对给定观测集的最佳解释，其中扩散历史是一个时间序列的传播图。该项目侧重于RDH的基本理论和高效的数据驱动算法。RDH的理论和算法在流行病学中可直接应用于识别受病毒感染的人，在网络安全中可用于跟踪计算机病毒/恶意软件的传播，以及在社交网络中查找泄露机密信息或谣言的来源和参与者。本项目的推力1通过部分观测建立了RDH的理论和基本极限，并回答了重建精度和计算复杂度如何随网络规模和数据样本的变化等基本问题。Thrust 2开发了一种基于深度学习的新算法基础，特别是在图神经网络和递归神经网络的交叉处，具有部分观测值。网络拓扑和时间动态被嵌入到细胞或神经元的设计和神经网络的结构中。开发的算法有望在准确性、可扩展性和适用性方面大大超过目前的技术水平。此外，理论和算法使用合成和现实世界的数据集进行评估。在这个项目下开发的新的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。

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.