# RelationPrediction

**Repository Path**: observer_of_the_world_line/RelationPrediction

## Basic Information

- **Project Name**: RelationPrediction
- **Description**: Implementation of R-GCNs for Relational Link Prediction
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-05
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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 out 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 (>1.0)

**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:

```
@article{schlichtkrull2017modeling,
  title={Modeling Relational Data with Graph Convolutional Networks},
  author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Berg, Rianne van den and Titov, Ivan and Welling, Max},
  journal={arXiv preprint arXiv:1703.06103},
  year={2017}
}
```