https://github.com/thiviyant/torch-rgcn
A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).
https://github.com/thiviyant/torch-rgcn
graph-convolutional-network graph-neural-network link-prediction node-classification relational-graphs
Last synced: 8 months ago
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A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).
- Host: GitHub
- URL: https://github.com/thiviyant/torch-rgcn
- Owner: thiviyanT
- License: mit
- Created: 2020-05-19T14:19:43.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-08-11T11:47:35.000Z (almost 5 years ago)
- Last Synced: 2025-09-05T06:11:50.623Z (9 months ago)
- Topics: graph-convolutional-network, graph-neural-network, link-prediction, node-classification, relational-graphs
- Language: Python
- Homepage:
- Size: 14 MB
- Stars: 128
- Watchers: 5
- Forks: 18
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Torch-RGCN
Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull *et al.* in
[Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103).
In our [paper](https://arxiv.org/abs/2107.10015), we reproduce the link prediction and node classification experiments from the original
paper and using our reproduction we explain the RGCN. Furthermore, we present two new configurations of the RGCN.
## Getting started
Requirements:
* Conda >= 4.8
* Python >= 3.7
Do the following:
1. Download all datasets: `bash get_data.sh`
2. Install the dependencies inside a new virtual environment: `bash setup_dependencies.sh`
3. Activate the virtual environment: `conda activate torch_rgcn_venv`
4. Install the torch-RGCN module: `pip install -e .`
## Usage
### Configuration files
The hyper-parameters for the different experiments can be found in [YAML](https://yaml.org/) files under
[configs](configs). The naming convention of the files is as follows: `configs/{MODEL}/{EXPERIMENT}-{DATASET}.yaml`
### Models
* `rgcn` - Standard RGCN Model
* `c-rgcn` - Compression RGCN Model
* `e-rgcn` - Embedding RGCN Model
### Experiments
* `lp` - Link Prediction
* `nc` - Node Classification
### Datasets
#### Link Prediction
* `WN18`
* `FB-Toy`
#### Node Classification
* `AIFB`
* `MUTAG`
* `BGS`
* `AM`
## Part 1: Reproduction
### Link Prediction
Original Link Prediction Implementation: https://github.com/MichSchli/RelationPrediction
To run the link prediction experiment using the RGCN model using:
`python experiments/predict_links.py with configs/rgcn/lp-{DATASET}.yaml`
Make sure to replace `{DATASET}` with one of the following dataset names: `FB-toy` or `WN18`.
### Node Classification
Original Node Classification Implementation: https://github.com/tkipf/relational-gcn
To run the node classification experiment using the RGCN model using:
`python experiments/classify_nodes.py with configs/rgcn/nc-{DATASET}.yaml`
Make sure to replace `{DATASET}` with one of the following dataset names: `AIFB`, `MUTAG`, `BGS` or `AM`.
## Part 2: New RGCN Configurations
### Node Classification with Node Embeddings
To run the node classification experiment use:
`python experiments/classify_nodes.py with configs/e-rgcn/nc-{DATASET}.yaml`
Make sure to replace `{DATASET}` with one of the following dataset names: `AIFB`, `MUTAG`, `BGS` or `AM`.
### Link Prediction Compressed Node Embeddings
To run the link prediction experiment use:
`python experiments/predict_links.py with configs/c-rgcn/lp-{DATASET}.yaml`
Make sure to replace `{DATASET}` with one of the following dataset names: `FB-toy`, or `WN18`.
---
### Dataset References
#### Node Classification
* `AIFB` from
Stephan Bloehdorn and York Sure.
*[Kernel methods for mining instance data in ontologies.](https://link.springer.com/content/pdf/10.1007%2F978-3-540-76298-0_5.pdf).*
In The Semantic Web, 6th International Semantic Web Conference, 2007.
* `MUTAG` from
A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J.Shusterman, and C. Hansch.
*[Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro-compounds correlation
with molecular orbital energies and hydrophobicity](https://pubs.acs.org/doi/pdf/10.1021/jm00106a046?casa_token=ECo0FUp3gNoAAAAA:6Xgkt3vGuQeVFnGwlPlyDWm-fIflRmsRe7s5X_SH143O4-wVz5eIMHj_cmDvBWCVon6LLvVt0nTgy-4).*
J Med Chem,34:786–797, 1991.
* `BGS` from
de Vries, G.K.D.
*[A fast approximation of the Weisfeiler-Lehman graph kernel for RDF data](https://link.springer.com/content/pdf/10.1007%2F978-3-642-40988-2_39.pdf).*
In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.
* `AM` from
de Boer, V., Wielemaker, J., van Gent, J., Hildebrand, M., Isaac, A., van Ossenbruggen, J., Schreiber, G.
*[Supporting linked data production for cultural heritageinstitutes: The amsterdam museum case study](https://link.springer.com/content/pdf/10.1007%2F978-3-642-30284-8_56.pdf).*
In The Semantic Web: Research and Applications, 2012.
#### Link Prediction
* `WN18` from
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran , Jason Weston, and Oksana Yakhnenko.
*[Translating embeddings for modeling multi-relational data](http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-rela)*.
In Advances in Neural Information Processing Systems, 2013.
* `FB-Toy` from
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla.
*[You CAN teach an old dog new tricks! on training knowledge graph embeddings](https://openreview.net/pdf?id=BkxSmlBFvr)*.
In International Conference on Learning Representations, 2019.