mlsanityData to 7 Oct 2026

A Survey on Graph Neural Networks for Knowledge Graph Completion

arXiv 2007.12374 ↗First posted 24 Jul 2020cs.CL cs.AI cs.LGcited by 26

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Abstract (arXiv)

Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance. As a result, there has been a lot of interest in the task of Knowledge Base Completion. More recently, Graph Neural Networks have been used to capture structural information inherently stored in these Knowledge Graphs and have been shown to achieve SOTA performance across a variety of datasets. In this survey, we understand the various strengths and weaknesses of the proposed methodology and try to find new exciting research problems in this area that require further investigation.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 2; 2024: 5; 2025: 2; 2026: 2.

26 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  3. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  4. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  5. 2022Deep Generative Model for Periodic Graphscited by 20
  6. 2022Geodesic Graph Neural Network for Efficient Graph Representation Learningcited by 19
  7. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 18
  8. 2021How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence Viewcited by 17
  9. 2021Graph Collaborative Reasoningcited by 14
  10. 2021Trans4E: Link Prediction on Scholarly Knowledge Graphscited by 13
  11. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  12. 2022Neural Graph Databasescited by 11
14 more
  1. 2024Continual Learning on Graphs: A Surveycited by 11
  2. 2024Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmarkcited by 7
  3. 2022Improving Question Answering over Knowledge Graphs Using Graph Summarizationcited by 6
  4. 2025Understanding and Mitigating Under-Confidence in GNNs from the Final Layercited by 6
  5. 2021Graph neural network-based fault diagnosis: a reviewcited by 5
  6. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  7. 2024Scalable Expressiveness through Preprocessed Graph Perturbationscited by 2
  8. 2021Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embeddingcited by 1
  9. 2023Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missingcited by 1
  10. 2023VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classificationcited by 0
  11. 2024Introduction to Graph Neural Networks for Machine Learning Engineerscited by 0
  12. 2025Higher-Order Graph Databasescited by 0
  13. 2026RADE: Random Add-Drop Edge as a Regularizercited by 0
  14. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0