The LDBC Social Network Benchmark
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.
Results and benchmarks
The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 70/100, grounding 75/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
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Time to first repro: a few days
ldbc/ldbc_snb_interactive_v1_impls is the closest maintained adjacent implementation (Matches contextual method/domain keyword: workload). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 114 GitHub stars.
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
- Adjacent implementation match confidence is low.
Reproduction readiness
No repo
No verified implementation available
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Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Repositories and ecosystem
Closest related implementations
These are not paper-verified. Use them as reference points when no direct implementation is available.
- ldbc/ldbc_snb_interactive_v1_impls Adjacent · Confidence: Low · 114 stars
Matches contextual method/domain keyword: workload
- ldbc/ldbc_snb_bi Adjacent · Confidence: Low · 47 stars
Matches contextual method/domain keyword: workload
No additional verified repositories beyond the primary recommendation.
These repositories had low-confidence matching signals and are hidden by default.
- ldbc/ldbc_snb_datagen_spark
Confidence: Low · 183 stars
Hugging Face artifacts
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Models
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Research context
266
Citations
11
References
Tasks
Benchmarking, Computer science, Workload, Benchmark (surveying), Testbed, Scalability, Graph, Distributed computing
Methods
None detected
Domains
Computer Vision and Pattern Recognition
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