RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage (Extended Version)

2026-01-04

arXiv.org

AI Summary

Efficient storage and updating of large-scale dynamic graphs face the challenge of balancing query performance against memory overhead. This work proposes a vertex indexing scheme based on a space-optimal pointer-array radix tree, integrated with a snapshot-log hybrid edge storage architecture, achieving amortized O(1) update complexity and strong support for high concurrency. The approach maintains high performance for graph analytics while significantly outperforming existing methods: it improves update throughput by up to 16.27× and reduces memory consumption by 40.1% on average.

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

Dynamic graphs model many real-world applications, and as their sizes grow, efficiently storing and updating them becomes critical. We present RadixGraph, a fast and memory-efficient data structure for dynamic graph storage. RadixGraph features a carefully designed radix-tree-based vertex index that strikes an optimal trade-off between query efficiency and space among all pointer-array-based radix trees. For edge storage, it employs a hybrid snapshot-log architecture that enables amortized $O(1)$ update time. RadixGraph supports millions of concurrent updates per second while maintaining competitive performance for graph analytics. Experimental results show that RadixGraph outperforms the most performant baseline by up to $16.27\times$ across various datasets in ingesting graph updates, and reduces memory usage by an average of $40.1\%$. RadixGraph is open-source at https://github.com/ForwardStar/RadixGraph.

Problem

• dynamic graph
• graph storage
• memory efficiency
• update efficiency
• data structure

Innovation

• RadixGraph
• dynamic graph storage
• radix-tree index
• snapshot-log architecture
• amortized O(1) update