# Hypernetwork Knowledge Graph Embeddings

arXiv: https://arxiv.org/abs/1808.07018 · HTML: https://mlsanity.com/paper/1808.07018/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 30 of 38)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [A Brief Review of Hypernetworks in Deep Learning](https://mlsanity.com/paper/2306.06955/) (2023)
- [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021)
- [Principled Weight Initialization for Hypernetworks](https://mlsanity.com/paper/2312.08399/) (2023)
- [Geometry Interaction Knowledge Graph Embeddings](https://mlsanity.com/paper/2206.12418/) (2022)
- [AutoGEL: An Automated Graph Neural Network with Explicit Link Information](https://mlsanity.com/paper/2112.01064/) (2021)
- [Convolutional Hypercomplex Embeddings for Link Prediction](https://mlsanity.com/paper/2106.15230/) (2021)
- [Searching to Sparsify Tensor Decomposition for N-ary Relational Data](https://mlsanity.com/paper/2104.10625/) (2021)
- [QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion](https://mlsanity.com/paper/2105.09002/) (2021)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding](https://mlsanity.com/paper/2104.10880/) (2021)
- [Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction](https://mlsanity.com/paper/2112.10644/) (2021)
- [A Review of Knowledge Graph Completion](https://mlsanity.com/paper/2208.11652/) (2022)
- [MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering](https://mlsanity.com/paper/2204.10629/) (2022)
- [ER: Equivariance Regularizer for Knowledge Graph Completion](https://mlsanity.com/paper/2206.12142/) (2022)
- [Out-of-Vocabulary Entities in Link Prediction](https://mlsanity.com/paper/2105.12524/) (2021)
- [Social Biases in Knowledge Representations of Wikidata separates Global North from Global South](https://mlsanity.com/paper/2505.02352/) (2025)
- [Adaptive Filters in Graph Convolutional Neural Networks](https://mlsanity.com/paper/2105.10377/) (2021)
- [FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics](https://mlsanity.com/paper/2111.00110/) (2021)
- [Kronecker Decomposition for Knowledge Graph Embeddings](https://mlsanity.com/paper/2205.06560/) (2022)
- [Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network](https://mlsanity.com/paper/2207.07503/) (2022)
- [Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition](https://mlsanity.com/paper/2212.05581/) (2022)
- [Diversity matters: Robustness of bias measurements in Wikidata](https://mlsanity.com/paper/2302.14027/) (2023)
- [MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph](https://mlsanity.com/paper/2505.17214/) (2025)
- [Knowledge Graph Embedding by Normalizing Flows](https://mlsanity.com/paper/2409.19977/) (2024)
- [Resilience in Knowledge Graph Embeddings](https://mlsanity.com/paper/2410.21163/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [A Statistical Relational Approach to Learning Distance-based GCNs](https://mlsanity.com/paper/2102.07007/) (2021)
- [Scalable knowledge base completion with superposition memories](https://mlsanity.com/paper/2110.12341/) (2021)
- [Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding](https://mlsanity.com/paper/2206.02963/) (2022)

## Abstract (arXiv)

Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes) is the task of link prediction. A recent state-of-the-art approach to link prediction, ConvE, implements a convolutional neural network to extract features from concatenated subject and relation vectors. Whilst results are impressive, the method is unintuitive and poorly understood. We propose a hypernetwork architecture that generates simplified relation-specific convolutional filters that (i) outperforms ConvE and all previous approaches across standard datasets; and (ii) can be framed as tensor factorization and thus set within a well established family of factorization models for link prediction. We thus demonstrate that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.