Higher-Order Graph Databases
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.
Cite
@article{arxiv.2506.19661,
title = {Higher-Order Graph Databases},
author = {Maciej Besta and Shriram Chandran and Jakub Cudak and Patrick Iff and Marcin Copik and Robert Gerstenberger and Tomasz Szydlo and Jürgen Müller and Torsten Hoefler},
journal= {arXiv preprint arXiv:2506.19661},
year = {2025}
}