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

2026-03-12

AI Summary

This work addresses the fundamental trade-off between update efficiency and traversal locality in in-memory dynamic graph systems under highly skewed degree distributions. To reconcile this tension, the authors propose a degree-aware hierarchical graph storage architecture that, for the first time, integrates learned indexes into graph data management. By decoupling vertex and edge access and adaptively organizing edge data using either lightweight arrays or learned indexes based on vertex degree, the design achieves both high update throughput and efficient graph traversals. Combined with a degree-aware memory layout and multicore-friendly memory management, the system optimizes performance for both frequent updates and complex analytical queries within a unified framework. Experimental results demonstrate 5.9–28.2× higher update throughput and significantly accelerated graph analysis compared to state-of-the-art in-memory graph systems.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• in-memory graph
• dynamic graph
• graph updates
• graph analytics
• degree distribution

Innovation

• learned indexing
• in-memory graph storage
• degree-aware hierarchy
• adaptive data structures
• graph analytics