4 citations · 8 across the 24 of their papers we have counts for
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cs.LG2026
NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
Zhongwei Xie, Jiaxin Bai, Shujie Liu +5
Neural Graph Databases (NGDBs) facilitate complex logical reasoning over incomplete knowledge structures, yet their training efficiency and expressivity are constrained by rigid qu…
cs.LG2024
Towards Subgraph Isomorphism Counting with Graph Kernels
Xin Liu, Weiqi Wang, Jiaxin Bai +1
Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution. Utilizing representation learning has been shown as a promising d…
cs.LG2024★ 1 cited
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
Qi Hu, Weifeng Jiang, Haoran Li +6
The increasing demand for deep learning-based foundation models has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) offer a compel…