https://github.com/kimmeen/neural-temporal-walks
[NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"
https://github.com/kimmeen/neural-temporal-walks
dynamic-graph-embedding dynamic-graphs graph-neural-networks graph-representation-learning
Last synced: 9 days ago
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[NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"
- Host: GitHub
- URL: https://github.com/kimmeen/neural-temporal-walks
- Owner: KimMeen
- License: mit
- Created: 2022-09-18T23:42:53.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-23T02:35:21.000Z (over 2 years ago)
- Last Synced: 2025-04-02T06:12:10.958Z (about 1 month ago)
- Topics: dynamic-graph-embedding, dynamic-graphs, graph-neural-networks, graph-representation-learning
- Language: Python
- Homepage:
- Size: 173 KB
- Stars: 53
- Watchers: 3
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
[](https://mingjin.dev/assets/pdf/neurips-22-jin.pdf)
[](https://www.youtube.com/watch?v=oUy6eSdOhms)
## Introduction
**[NIPS 2022]** The official PyTorch implementation of "[Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs](https://mingjin.dev/assets/pdf/neurips-22-jin.pdf)"
## Authors
[Ming Jin](https://mingjin.dev/), [Yuan-Fang Li](https://users.monash.edu.au/~yli/), [Shirui Pan](https://shiruipan.github.io/)
## Requirements
- matplotlib==3.3.4
- numba==0.54.1
- numpy==1.19.2
- pandas==1.2.2
- scikit_learn==1.1.1
- torch==1.7.1
- torchdiffeq==0.2.2
- tqdm==4.59.0
To install all dependencies:
```
pip install -r requirements.txt
```
## Dataset and preprocessing
### Option 1. Use our preprocessed datasets
- Click [here](https://drive.google.com/uc?export=download&id=1jao1WgVt6VKfDA4KNPfxqlH0CeBNNL6Y) to download our preprocessed datasets.
- Unzip the downloaded file
- Place all dataset files under the ./data directory
### Option 2. Preprocess datasets by yourself
- Please refer to our paper to download the raw datasets
- Put the dataset files under the ./data directory
- Use the provided scripts to preprocess the raw dataset ```.csv``` files. For example:
- For the CollegeMsg dataset, you can run our script directly to preprocess it: ```python collegemsg.py```
- For the Taobao dataset, you need to execute ```taobao_preprocessing.ipynb``` first before running ```python taobao.py```
## Model training
Here we provide three examples. Firstly, enter the directory with training scripts:
```cd scripts/```
To train on the CollegeMsg dastaset:
- Inductive: ```bash collegemsg_inductive.sh```
- Transductive: ```bash collegemsg_transductive.sh```
To train on the Enron dastaset:
- Inductive: ```bash enron_inductive.sh```
- Transductive: ```bash enron_transductive.sh```
To train on the Taobao dastaset:
- Inductive: ```bash taobao_inductive.sh```
- Transductive: ```bash taobao_transductive.sh```
## Detailed usage
Please refer to the function ```get_args()``` in ```util.py``` for the detailed description of each hyperparameter.
## Acknowledgement
Our implementation adapts the code of [TGAT](https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs) and [CAWs](https://github.com/snap-stanford/CAW) as the code base and extensively adapts it to our purpose. We thank the authors for sharing their code.
## Cite us
If you find this research useful, please cite our paper:
```
@inproceedings{
jin2022neural,
title={Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs},
author={Ming Jin and Yuan-Fang Li and Shirui Pan},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022}
}
```