mlsanityData to 7 Oct 2026

KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

arXiv 2205.02460 ↗First posted 5 May 2022cs.LGcited by 7

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Abstract (arXiv)

While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properties of different HPs and measure the transfer ability from small subgraph to the full graph. Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage. Experiments show that our method can consistently find better HPs than the baseline algorithms within the same time budget, which achieves 9.1% average relative improvement for four embedding models on the large-scale KGs in open graph benchmark.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 62 references that are in the map. First posted on arXiv 5 May 2022.

Builds on

29 of 62 references.

References · 29

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17 more
  1. 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
  2. 2020Design Space for Graph Neural Networkscited by 182
  3. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  4. 2018Tunability: Importance of Hyperparameters of Machine Learning Algorithmscited by 98
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  6. 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
  7. 2019Learning to Exploit Long-term Relational Dependencies in Knowledge Graphscited by 83
  8. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
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  10. 2020Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Frameworkcited by 70
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  13. 2019AutoSF: Searching Scoring Functions for Knowledge Graph Embeddingcited by 34
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  15. 2020Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completioncited by 28
  16. 2021BORE: Bayesian Optimization by Density-Ratio Estimationcited by 26
  17. 2017On Multi-Relational Link Prediction with Bilinear Modelscited by 17

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 1.

7 papers.

  1. 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sourcescited by 31
  2. 2025GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environmentscited by 18
  3. 2022Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learningcited by 12
  4. 2023Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channelcited by 10
  5. 2023Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filteringcited by 3
  6. 2022Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddingscited by 1
  7. 2024Towards Versatile Graph Learning Approach: from the Perspective of Large Language Modelscited by 1