# TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Prediction

arXiv: https://arxiv.org/abs/1910.00702 · HTML: https://mlsanity.com/paper/1910.00702/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 17 of 17)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
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- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [A Review of Knowledge Graph Completion](https://mlsanity.com/paper/2208.11652/) (2022)
- [Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes](https://mlsanity.com/paper/2111.06854/) (2021)
- [QDC: Quantum Diffusion Convolution Kernels on Graphs](https://mlsanity.com/paper/2307.11234/) (2023)
- [Node Duplication Improves Cold-start Link Prediction](https://mlsanity.com/paper/2402.09711/) (2024)
- [Network Representation Learning: From Traditional Feature Learning to Deep Learning](https://mlsanity.com/paper/2103.04339/) (2021)
- [Narrative Cartography with Knowledge Graphs](https://mlsanity.com/paper/2112.00970/) (2021)
- [Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning](https://mlsanity.com/paper/2205.06456/) (2022)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [Relational Graph Convolutional Networks Do Not Learn Sound Rules](https://mlsanity.com/paper/2408.10261/) (2024)
- [Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models](https://mlsanity.com/paper/2605.06154/) (2026)
- [Logical Expressivity and Explanations for Monotonic GNNs with Scoring Functions](https://mlsanity.com/paper/2508.14091/) (2025)
- [Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs](https://mlsanity.com/paper/2605.05463/) (2026)
- [A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks](https://mlsanity.com/paper/2605.05476/) (2026)

## Abstract (arXiv)

Link prediction is an important and frequently studied task that contributes to an understanding of the structure of knowledge graphs (KGs) in statistical relational learning. Inspired by the success of graph convolutional networks (GCN) in modeling graph data, we propose a unified GCN framework, named TransGCN, to address this task, in which relation and entity embeddings are learned simultaneously. To handle heterogeneous relations in KGs, we introduce a novel way of representing heterogeneous neighborhood by introducing transformation assumptions on the relationship between the subject, the relation, and the object of a triple. Specifically, a relation is treated as a transformation operator transforming a head entity to a tail entity. Both translation assumption in TransE and rotation assumption in RotatE are explored in our framework. Additionally, instead of only learning entity embeddings in the convolution-based encoder while learning relation embeddings in the decoder as done by the state-of-art models, e.g., R-GCN, the TransGCN framework trains relation embeddings and entity embeddings simultaneously during the graph convolution operation, thus having fewer parameters compared with R-GCN. Experiments show that our models outperform the-state-of-arts methods on both FB15K-237 and WN18RR.