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The LDBC Graphalytics Benchmark

Alexandru Iosup, Ahmed Musaafir, Alexandru Uta, Arnau Prat-Pèrez, Gábor Szárnyas +13 morePublished Nov 30, 2020
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few days
Plan setup time
Risk flags
1
Review before use

Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective comparison of graph analysis platforms. Due to the diversity of bottlenecks and performance issues such platforms need to address, Graphalytics consists of a set of selected deterministic algorithms for full-graph analysis, standard graph datasets, synthetic dataset generators, and reference output for validation purposes. Its test harness produces deep metrics that quantify multiple kinds of systems scalability, weak and strong, and robustness, such as failures and performance variability. The benchmark also balances comprehensiveness with runtime necessary to obtain the deep metrics. The benchmark comes with open-source software for generating performance data, for validating algorithm results, for monitoring and sharing performance data, and for obtaining the final benchmark result as a standard performance report.

Results and benchmarks

Freshness tier: cold
In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms.

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.

Implementation evidence summary
Confidence: low

ldbc/ldbc_graphalytics is the closest maintained adjacent implementation (Strong overlap with paper title keywords). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 88 GitHub stars.

Reproduction risks
  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.
  • Adjacent implementation match confidence is low.

Reproduction readiness

Time to first repro: days
Last checked: Aug 24, 2026

No repo

No verified implementation available

  • No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

Hardware requirements

  • Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Repositories and ecosystem

Closest related implementations

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No additional verified repositories beyond the primary recommendation.

Hugging Face artifacts

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Research context

3

Citations

40

References

Tasks

Benchmark (surveying), Computer science, Scalability, Robustness (evolution), Graph, Software, Data mining, Set (abstract data type)

Methods

None detected

Domains

Computer Vision and Pattern Recognition

Evaluation and human feedback data

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