DUPLEX: Dual Graph Attention Network for Complex Embedding of Directed Graphs

This PyTorch implementation showcases the DUPLEX model as described in the paper [DUPLEX: Dual GAT for Complex Embedding of Directed Graphs].

Figure 1: The network architecture of DUPLEX.

Requirements

• Ubuntu OS
• Python 3.8
• PyTorch 2.0.1
• CUDA 11.4

You can install the necessary dependencies using the following command:

conda env create -f environment.yml

Data Preparation

To preprocess data, follow these steps:

1. Navigate to the ./code/ directory using the command cd ./code/.
2. Open the ./generate_data.ipynb notebook to generate DGL graphs from raw data.
3. Execute python ./train_edge/split_data.py to create the train/validation/test sets for the link prediction task.
4. Execute python ./train_node_ind/process_data.py to create the train/validation/test sets for the node classification task.

Training & Evaluation

To conduct experiments, follow these steps:

1. Navigate to the ./code/ directory using the command cd ./code/.
2. Execute python ./train_edge/train.py for link prediction.
3. Execute python ./train_node_trans/train.py for transductive node classification.
4. Execute python ./train_node_ind/train.py for inductive node classification.

For details on each command-line argument, please refer to the explanations provided in the respective training scripts.

Citation

TODO

Contact

For any questions related to DUPLEX, please submit them to Github Issues.