NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training

• URL: http://arxiv.org/abs/2602.21597v1
• Date: Wed, 25 Feb 2026 05:46:42 GMT
• Title: NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
• Authors: Zhongwei Xie, Jiaxin Bai, Shujie Liu, Haoyu Huang, Yufei Li, Yisen Gao, Hong Ting Tsang, Yangqiu Song,
• Abstract summary: We present NGDB-Zoo, a unified framework that resolves bottlenecks by synergizing operator-level training with semantic augmentation.<n>We demonstrate that NGDB-Zoo maintains high GPU utilization across diverse logical patterns and significantly mitigates friction in hybrid neuro-symbolic reasoning.
• Score: 55.35217340229661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Graph Databases (NGDBs) facilitate complex logical reasoning over incomplete knowledge structures, yet their training efficiency and expressivity are constrained by rigid query-level batching and structure-exclusive embeddings. We present NGDB-Zoo, a unified framework that resolves these bottlenecks by synergizing operator-level training with semantic augmentation. By decoupling logical operators from query topologies, NGDB-Zoo transforms the training loop into a dynamically scheduled data-flow execution, enabling multi-stream parallelism and achieving a $1.8\times$ - $6.8\times$ throughput compared to baselines. Furthermore, we formalize a decoupled architecture to integrate high-dimensional semantic priors from Pre-trained Text Encoders (PTEs) without triggering I/O stalls or memory overflows. Extensive evaluations on six benchmarks, including massive graphs like ogbl-wikikg2 and ATLAS-Wiki, demonstrate that NGDB-Zoo maintains high GPU utilization across diverse logical patterns and significantly mitigates representation friction in hybrid neuro-symbolic reasoning.

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