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arXiv · 2603.11596

LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics

Pengpeng Qiao·Zhiwei Zhang·Xinzhou Wang·Zhetao Li·Xiaochun Cao·Yang Cao
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Abstract

Various real-world applications rely on in-memory dynamic graphs that must efficiently handle frequent updates while supporting low-latency analytics on evolving structures. Achieving both objectives remains challenging due to the trade-off between update efficiency and traversal locality, particularly under highly skewed degree distributions. This motivates the design of graph indexing schemes optimized for in-memory graph management on modern multi-core CPUs. We present LHGstore, a degree-aware Learned Hierarchical Graph storage that, for the first time, integrates learned indexing into graph management. LHGstore designs a two-level hierarchy that decouples vertex and edge access and further organizes each vertex's edges using data structures adaptive to its degree. Lightweight arrays are used for low-degree vertices to maximize traversal locality, while learned indexes are applied to high-degree vertices to improve update throughput. Extensive experiments show that LHGstore achieves 5.9-28.2$\times$ higher throughput and significantly faster analytics than SOTA in-memory graph storage systems.

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Pengpeng Qiao, Zhiwei Zhang, Xinzhou Wang, Zhetao Li, Xiaochun Cao, Yang Cao. 2026-03-12. LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics. https://arxiv.org/abs/2603.11596

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