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RelationPrediction

(★ 466)

Implementation of R-GCNs for Relational Link Prediction

파일 탐색기

최종 버전 다운로드 (.zip)

- workspace.xml
- - __init__.py
  - auxilliaries.py
  - evaluation.py
  - io.py
  - model_builder.py
  - optimizer_parameter_parser.py
  - settings_reader.py
  - shared_functions.py
- - __init__.py
  - bilinear_diag.py
  - complex.py
  - nonlinear_transform.py
- - __init__.py
  - affine_transform.py
  - bipartite_gcn.py
  - direct_message_gcn.py
  - random_vertex_embedding.py
  - relation_embedding.py
- - __init__.py
  - dropover.py
  - graph_representations.py
  - highway_layer.py
  - residual_layer.py
  - variational_encoding.py
- - __init__.py
  - abstract.py
  - optimize.py
  - setup.py
- - __init__.py
  - cluster.py
  - dictionaries.py
  - ensemble.py
  - make_degree_dataset.py
  - make_single_label_dataset.py
  - make_split_dataset.py
- __init__.py
- model.py
- split_model.py
- train.py
- - entities.dict
  - README.txt
  - relations.dict
  - test.txt
  - train.txt
  - valid.txt
- - entities.dict
  - relations.dict
  - test.txt
  - train.txt
  - valid.txt
- - entities.dict
  - relations.dict
  - test.txt
  - train.txt
  - valid.txt
- - entities.dict
  - README
  - relations.dict
  - test.txt
  - train.txt
  - valid.txt
  - Wordnet3.0-LICENSE
- complex.exp
- distmult.exp
- gcn_basis.exp
- gcn_block.exp
.gitignore
LICENSE
README.md
run-train.sh
run-train_slurm.sh

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/MichSchli/RelationPrediction

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd RelationPrediction

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

⚠️ 이 프로젝트는 규모가 큰 저장소라, 이 방법이 실제 핵심 제품이 아니라 내부 하위 패키지를 가리키는 것일 수 있습니다. README 전체를 함께 확인해보세요.

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Graph Convolutional Networks for Relational Link Prediction

This repository contains a TensorFlow implementation of Relational Graph Convolutional Networks (R-GCN), as well as experiments on relational link prediction. The description of the model and the results can be found in our paper:

[Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103). Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling (ArXiv 2017)

**Requirements**

* TensorFlow (1.4)

**Running demo**

We provide a bash script to run a demo of our code. In the folder *settings*, a collection of configuration files can be found. The block diagonal model used in our paper is represented through the configuration file *settings/gcn_block.exp*. To run a given experiment, execute our bash script as follows:

```
bash run-train.sh \[configuration\]
```

We advise that training can take up to several hours and require a significant amount of memory.

**Citation**

Please cite our paper if you use this code in your own work:

```
@inproceedings{schlichtkrull2018modeling,
  title={Modeling relational data with graph convolutional networks},
  author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and {van den Berg}, Rianne and Titov, Ivan and Welling, Max},
  booktitle={The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, Proceedings 15},
  pages={593--607},
  year={2018},
  organization={Springer}
}
```