Higher-Order Graph Databases

TL;DR

This paper introduces higher-order graph databases (HO-GDBs) that extend traditional graph databases to support complex higher-order interactions, improving analytical capabilities and neural network accuracy.

Contribution

It presents a new theoretical framework and a practical prototype for HO-GDBs, enabling native support for hypergraphs and higher-order structures with scalable, ACID-compliant query processing.

Findings

Prototype scales to large workloads

HO improves graph neural network accuracy by 44%

Supports ACID and eventual consistency

Abstract

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.