This repository contains the source code and datasets for Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder, published in NeurIPs 2023 (GFrontiers).

• Requirements
• Overview
• Data
• Multiplex Representation Learning with METAG
• Downstream Task Inference
• Citations

The code is written in Python 3.8. Before running, you need to first install the required packages by typing following commands (Using a virtual environment is recommended):

pip3 install -r requirements.txt

METAG contains one language model encoder to learn multiplex representations for nodes on text-attributed graphs.

You can directly download the processed datasets here without conducting the following steps.

MAG data and Amazon data can be downloaded here: MAG Link and Amazon Link.

1. Run the cells in tools/generate_data_{mag/amazon}.ipynb to generate sampled data for relation representation learning.
2. Run src/scripts/prepare_data.sh to obtain processed data (random sample and split into train/val/test).

1. Run the cells in tools/generate_downstream_{mag/amazon}.ipynb to generate data for downstream tasks.

Train:

cd src/
bash run_train_{MAG/amazon}.sh

Test:

bash run_test_group_{MAG/amazon}.sh

bash downstream_match_test_zeroshot.sh

bash downstream_{class/regression/match}_train.sh
bash downstream_{class/regression/match}_test.sh

Please cite the following paper if you find the code helpful for your research.

@article{jin2023learning,
  title={Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder},
  author={Jin, Bowen and Zhang, Wentao and Zhang, Yu and Meng, Yu and Zhao, Han and Han, Jiawei},
  journal={arXiv preprint arXiv:2310.06684},
  year={2023}
}