mlsanityData to 7 Oct 2026

Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

arXiv 2104.07396 ↗First posted 15 Apr 2021cs.CL cs.LGcited by 12

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Abstract (arXiv)

We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e., link prediction). Given a knowledge graph, NoGE constructs a single graph considering entities and relations as individual nodes. NoGE then computes weights for edges among nodes based on the co-occurrence of entities and relations. Next, NoGE proposes Dual Quaternion Graph Neural Networks (DualQGNN) and utilizes DualQGNN to update vector representations for entity and relation nodes. NoGE then adopts a score function to produce the triple scores. Comprehensive experimental results show that NoGE obtains state-of-the-art results on three new and difficult benchmark datasets CoDEx for knowledge graph completion.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

14 of 29 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2016Complex Embeddings for Simple Link Predictioncited by 897
  6. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  7. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  8. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  9. 2019Quaternion Knowledge Graph Embeddingscited by 175
  10. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  11. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  12. 2020CoDEx: A Comprehensive Knowledge Graph Completion Benchmarkcited by 102
2 more
  1. 2019Universal Graph Transformer Self-Attention Networkscited by 41
  2. 2020Quaternion Graph Neural Networkscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

12 papers.

  1. 2022Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregationcited by 18
  2. 2022Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representationcited by 8
  3. 2024Deep Outdated Fact Detection in Knowledge Graphscited by 6
  4. 2021Two-view Graph Neural Networks for Knowledge Graph Completioncited by 3
  5. 2022Joint Multilingual Knowledge Graph Completion and Alignmentcited by 3
  6. 2024Fully Hyperbolic Rotation for Knowledge Graph Embeddingcited by 3
  7. 2023Event Prediction using Case-Based Reasoning over Knowledge Graphscited by 2
  8. 2023Marginal Nodes Matter: Towards Structure Fairness in Graphscited by 2
  9. 2024Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approachcited by 1
  10. 2025Improved Masked Image Generation with Knowledge-Augmented Token Representationscited by 1
  11. 2024HyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graphcited by 0
  12. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0