Collaborative Research: ATD: Fast Algorithms and Novel Continuous-depth Graph Neural Networks for Threat Detection

In algorithmic threat detection, understanding the interactions of multivariate time series is crucial. Graph neural networks (GNNs) with attention mechanisms have proven effective in learning and predicting such time series. This project aims to investigate GNNs for improved acceleration and accuracy. The research will have broad applicability in fields such as Artificial Intelligence (AI), traffic analysis, power systems, and health analytics. The project will provide training opportunities and promote STEM education for underrepresented students. The project aims to address three key challenges in threat detection within multivariate time series: 1) maintaining accuracy with deep GNNs, 2) training GNNs with limited data, and 3) reducing computational costs in training and deploying deep GNNs with attention layers. The research advances continuous-depth GNNs and efficient attention algorithms based on the partial differential equation (PDE) theory. By leveraging the continuous viewpoint of GNNs, the project aims to develop theoretically-grounded and computationally efficient algorithms for accurate graph deep learning with limited supervision. The project will focus on three research thrusts: Thrust A: Bridging diffusion equation theory and GNN architecture design to develop a new class of GNNs based on diffusion equations on graphs. These GNNs overcome over-smoothing and reliably learn and predict with limited supervision. Thrust B: Developing fast algorithms for GNN and attention training, testing, and inference. Thrust C: Application of the new algorithms to anomaly detection and software development, specifically in benchmark graph learning tasks and anomaly detection in traffic flow, power distribution, and epidemic data. 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.