Higher-Order Graph Databases
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918069200224256 |
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| author | Besta, Maciej Chandran, Shriram Cudak, Jakub Iff, Patrick Copik, Marcin Gerstenberger, Robert Szydlo, Tomasz Müller, Jürgen Hoefler, Torsten |
| author_facet | Besta, Maciej Chandran, Shriram Cudak, Jakub Iff, Patrick Copik, Marcin Gerstenberger, Robert Szydlo, Tomasz Müller, Jürgen Hoefler, Torsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Higher-Order Graph Databases Besta, Maciej Chandran, Shriram Cudak, Jakub Iff, Patrick Copik, Marcin Gerstenberger, Robert Szydlo, Tomasz Müller, Jürgen Hoefler, Torsten Databases Information Retrieval Machine Learning Social and Information Networks 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. |
| title | Higher-Order Graph Databases |
| topic | Databases Information Retrieval Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2506.19661 |