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.

翻译：各类现实应用依赖于内存动态图，这些系统必须高效处理频繁更新，同时在演化结构上支持低延迟分析。由于更新效率与遍历局部性之间的权衡，尤其是在高度偏斜的度分布下，同时实现这两个目标仍具挑战性。这促使我们设计针对现代多核CPU内存图管理优化的图索引方案。本文提出LHGstore，一种度感知的学习型层次化图存储系统，首次将学习索引技术集成至图管理。LHGstore设计了两层层次结构，解耦顶点与边的访问，并进一步根据顶点度自适应选择数据结构组织其邻边。低度顶点采用轻量级数组以最大化遍历局部性，而高度顶点则应用学习索引以提升更新吞吐量。大量实验表明，相较于当前最优的内存图存储系统，LHGstore实现了5.9-28.2倍的吞吐量提升，并显著加快了分析速度。