提出支持高阶关系的图数据库，提升图神经网络精度44%。
Higher-Order Graph Databases
- 用升维降维机制扩展传统图数据库，支持超图等高阶结构
- 在真实负载下实现低延迟高吞吐，图神经网络准确率提升44%
- 适合需要复杂关系建模的科研与工业分析场景
近年来图数据库的发展推动了大规模数据分析，但现有系统无法支持超越一阶关系的高阶（HO）交互，而这类关系对子图计数、多价建模和高阶图学习至关重要。本文提出一类新系统——高阶图数据库（HO-GDB），通过升维与降维范式，无缝扩展传统图数据库以支持高阶结构。我们对OLTP与OLAP查询进行了理论分析，确保正确性、可扩展性及ACID一致性。实现了一个轻量、模块化且可并行的HO-GDB原型，原生支持超图、节点元组、子图等高阶结构，并通过统一API进行操作。该原型可扩展至大规模高阶OLTP与OLAP工作负载，显著提升分析任务性能，例如在图数据库中使用时，使图神经网络准确率提升44%。系统兼具低延迟、高吞吐特性，适用于既要求ACID一致性又需最终一致性的场景。
原文摘要 · Abstract (English)
Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order (one-hop) relations, which are crucial for tasks such as subgraph counting, polyadic modeling, and HO graph learning. We address this by introducing a new class of systems, higher-order graph databases (HO-GDBs) that use lifting and lowering paradigms to seamlessly extend traditional GDBs with HO. We provide a theoretical analysis of OLTP and OLAP queries, ensuring correctness, scalability, and ACID compliance. We implement a lightweight, modular, and parallelizable HO-GDB prototype that offers native support for hypergraphs, node-tuples, subgraphs, and other HO structures under a unified API. The prototype scales to large HO OLTP & OLAP workloads and shows how HO improves analytical tasks, for example enhancing accuracy of graph neural networks within a GDB by 44%. Our work ensures low latency and high query throughput, and generalizes both ACID-compliant and eventually consistent systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。