Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs

Introduction

[NIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"

Authors

Ming Jin, Yuan-Fang Li, Shirui Pan

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 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 and CAWs 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}
}