The LDBC Social Network Benchmark
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Abstract (arXiv)
The Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB) is an effort intended to test various functionalities of systems used for graph-like data management. For this, LDBC SNB uses the recognizable scenario of operating a social network, characterized by its graph-shaped data. LDBC SNB consists of two workloads that focus on different functionalities: the Interactive workload (interactive transactional queries) and the Business Intelligence workload (analytical queries). This document contains the definition of both workloads. This includes a detailed explanation of the data used in the LDBC SNB, a detailed description for all queries, and instructions on how to generate the data and run the benchmark with the provided software.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021Intelligent Software Web Agents: A Gap Analysiscited by 2
- 2023BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphscited by 2
- 2026Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Enginescited by 0
- 2026Graph Memory for LLM Agents: At What Cost? A Comparative Evaluation of Query, Ingest, and Update Performance Across Graph Database Engines20 Sep 2026
- 2023BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs23 Jun 2023
- 2021Intelligent Software Web Agents: A Gap Analysis12 Feb 2021