Learning Attribute-Structure Co-Evolutions in Dynamic Graphs

Description: This repository contains the reference implementation of CoEvoGNN model proposed in the paper Learning Attribute-Structure Co-Evolutions in Dynamic Graphs accepted by The Second International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD’20) and won the Best Paper Award.

Usage

1. Dependencies

This code package was developed and tested with Python 3.7 and PyTorch 1.0.1.post2. Make sure all dependencies specified in the ./requirements.txt file are satisfied before running the model. This can be achieved by

pip install -r requirements.txt

Other environment management tool such as Conda can also be used.

2. Data

The ./data/ folder contains the evolutionary co-authorship graphs (2k and 10k) descripted in the paper. Predefined paths for locating necessary data files can be found in the ./config.py file.

Note: Due to file size constraints, the 10k dataset files are compressed inside ./data/data_10k.zip. Please unzip the file for training on the dataset.

3. Run

To train the model, run

python main.py --dataset 2k --epochs 10

List of arguments:

• --dataset: The dataset of evolutionary co-authorship graphs to use. Valid choices include 2k and 10k. Default is 2k
• --t_0: The start index of available time points. Default is 0
• --T: Length of available training time points. Default is 8
• --t_train: Length of training time points (from --t_0). Default is 8
• --t_forecast: Number of forecasting snapshots. Default is 1
• --K: Num of layers fusing new time point. Default is 2
• --epochs: Number of epochs for training. Default is 10
• --H_0_npf: File for initializing H_0.

Examples

Other examples are provided in the ./example.sh file.

Miscellaneous

If you find this code pacakage is helpful, please consider cite us:

@article{wang2020learning,
  title={Learning Attribute-Structure Co-Evolutions in Dynamic Graphs},
  author={Wang, Daheng and Zhang, Zhihan and Ma, Yihong and Zhao, Tong and Jiang, Tianwen and Chawla, Nitesh V and Jiang, Meng},
  journal={arXiv preprint arXiv:2007.13004},
  year={2020}
}