Neural Empowered Subgraph Query Processing. This project aims to develop a neural-empowered model for subgraph query processing, which has many critical applications such as cybersecurity, biomedicine

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Neural Empowered Subgraph Query Processing. 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.. Scheme: Discovery Early Career Researcher Award. Field: 4605 - Data Management and Data Science. Lead: Dr Hanchen Wang