Neural Empowered Subgraph Query Processing. $467,857, Sole Investigator, Jan 2025 - Dec 2027.

This project aims to develop a neural-empowered model for subgraph query processing, which has many critical applications such as cybersecurity, biomedicine, and e-commerce. In particular, this project will focus on three core and quintessential subgraph queries: subgraph matching, subgraph counting, and community search. The innovative techniques developed for these queries will be integrated into a unified model, with large graph models as the backbone, heralding a new era of graph databases. Success in this project will establish a foundational advancement in artificial intelligence for databases, offering substantial benefits for applications like cybersecurity, health, and e-commerce.