While AI systems have made remarkable progress in processing unstructured text, structured data such as graphs stored in databases, continues to grow rapidly yet remains difficult for neural models to effectively utilize. We introduce NGDBench, a unified benchmark for evaluating neural graph database capabilities across five diverse domains, including finance, medicine, and AI agent tooling. Unlike prior benchmarks limited to elementary logical operations, NGDBench supports the full Cypher query language, enabling complex pattern matching, variable-length paths, and numerical aggregations, while incorporating realistic noise injection and dynamic data management operations. Our evaluation of state-of-the-art LLMs and RAG methods reveals significant limitations in structured reasoning, noise robustness, and analytical precision, establishing NGDBench as a critical testbed for advancing neural graph data management. Our code and data are available at https://github.com/HKUST-KnowComp/NGDBench.
@article{arxiv.2603.05529,
title = {Towards Neural Graph Data Management},
author = {Yufei Li and Yisen Gao and Jiaxin Bai and Jiaxuan Xiong and Haoyu Huang and Zhongwei Xie and Hong Ting Tsang and Yangqiu Song},
journal= {arXiv preprint arXiv:2603.05529},
year = {2026}
}