. This is the foundational paper describing its core design goals, including factorized query processing and optimized join algorithms for large-scale graph analysis. Graph Learning Application:

If you are looking for current academic publications on Kùzu to cite or use as a template, these are the primary sources: The Vision Paper: "KŮZU Graph Database Management System" (CIDR 2023)

"Kùzu: Graph Learning Applications Need a Modern Graph DBMS"

, exploring how vector indexes can be implemented as a graph within the database. Recommended Structure for a New Paper

. This focuses on how Kùzu fills the gap for machine learning pipelines by efficiently exporting data to libraries like PyTorch Geometric Future Directions: A paper on Kùzu's native vector index is slated for

If you are writing an original technical report or research paper, use this structure based on Kùzu's technical strengths: KŮZU^* Graph Database Management System - CIDR

To "make a paper" on —an extremely fast, embeddable graph database—you should focus on its unique architecture designed for "beyond relational" analytical workloads. Kùzu is often called the " DuckDB for graphs