Skip to content
context only
Benchmarks: missing
Time to repro: a few days
1 risk flag

Results & Benchmarks

Freshness tier: cold
Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs presents a algorithm approach for computer science.

Implementation Evidence Summary

Confidence: medium

doujiang-zheng/Awesome-Graph-Learning-Papers-List is the closest maintained adjacent implementation (Matches contextual method/domain keyword: graph). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 235 GitHub stars.

Reproduction Risks

  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.

Hardware Notes

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

Evidence disclosure

Evidence graph: 3 refs, 3 links.

Utility signals: depth 65/100, grounding 75/100, status medium.

Implementation Comparison

Top 1 paths

Compare maintenance quality, reproducibility coverage, and evidence confidence before choosing a reproduction baseline.

shenyangHuang/TGB
alternative
Maintenance: Stale risk
Confidence: Low
Reproducibility: Strong

Strong overlap with paper title keywords · Community adoption signal (257 stars)

Stars
257
Last push
Oct 6, 2025 (257d ago)
CIReleasesDependencies

Risk flags

  • No Docker setup
  • Low confidence match

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

  • No maintained paper-verified implementation was found; start with the closest related repositories below.
  • Compare repo methods against the paper equations/algorithm before trusting metrics.
  • Create a minimal baseline implementation from the paper and use adjacent repos as references.
Time to first repro: a few days

Reproduction readiness

No Repo
Time to first repro: days
Last checked: Jun 18, 2026

Hardware requirements

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

No verified implementation available

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

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Closest related implementations

These are not paper-verified. Use them as reference points when no direct implementation is available.

Additional implementations

No additional verified repositories beyond the primary recommendation.

These repositories had low-confidence matching signals and are hidden by default.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet.

Continue with targeted Hugging Face searches derived from the paper title and method context:

Tip: start with models, then check datasets/spaces if you need evaluation data or demos.

Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.

Search models Search datasets Search spaces

Research context

2

Citations

0

References

Tasks

Computer science, Benchmark (surveying), Theoretical computer science, Knowledge graph, Graph, Task (project management), Feature (linguistics), Generalization

Methods

Algorithm

Domains

Artificial intelligence, Mathematics, Machine learning

Evaluation & Human Feedback Data

Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.

Open in HFEPX

Explore Similar Papers

Jump to Paper2Code search queries derived from this paper's research context.

Computer science Benchmark (surveying) Theoretical computer science Knowledge graph Graph Task (project management)

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote