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University of Illinois at Urbana-Champaign
Illinois
Computer and Information Science and Engineering (CISE)
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.