# RE-GCN

**Repository Path**: Tomhappy/RE-GCN

## Basic Information

- **Project Name**: RE-GCN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-08-22
- **Last Updated**: 2026-08-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

This is the official code release of the following paper: 

Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang and Xueqi Cheng. [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://arxiv.org/abs/2104.10353). SIGIR 2021.

<img src="https://github.com/Lee-zix/RE-GCN/blob/master/img/regcn.png" alt="regcn_architecture" width="700" class="center">

## Quick Start

### Environment variables & dependencies
```
conda create -n regcn python=3.7

conda activate regcn

pip install -r requirement.txt
```

### Process data
First, unzip and unpack the data files 
```
tar -zxvf data-release.tar.gz
```
For the three ICEWS datasets `ICEWS18`, `ICEWS14`, `ICEWS05-15`, go into the dataset folder in the `./data` directory and run the following command to construct the static graph.
```
cd ./data/<dataset>
python ent2word.py
```

### Train models
Then the following commands can be used to train the proposed models. By default, dev set evaluation results will be printed when training terminates.

0. Make dictionary to save models
```
mkdir models
```

1. Train models
```
cd src
python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0
```

### Evaluate models
To generate the evaluation results of a pre-trained model, simply add the `--test` flag in the commands above. 

For example, the following command performs single-step inference and prints the evaluation results (with ground truth history).
```
python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --test
```

The following command performs multi-step inference and prints the evaluation results (without ground truth history).
```
python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --test --multi-step --topk 0
```


### Change the hyperparameters
To get the optimal result reported in the paper, change the hyperparameters and other experiment set up according to Section 5.1.4 in the paper (https://arxiv.org/abs/2104.10353). 


## Citation
If you find the resource in this repository helpful, please cite
```
@article{li2021temporal,
  title={Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning},
  author={Li, Zixuan and Jin, Xiaolong and Li, Wei and Guan, Saiping and Guo, Jiafeng and Shen, Huawei and Wang, Yuanzhuo and Cheng, Xueqi},
  booktitle={SIGIR},
  year={2021}
}
```