Graph Transformer for Label Placement

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This repository is the implementation of the paper:

Jingwei Qu, Pingshun Zhang, Enyu Che, Yinan Chen, and Haibin Ling. Graph Transformer for Label Placement. TVCG, 2024.

It contains the training and evaluation procedures in the paper.

Requirements

• Python (>= 3.10.12)
• PyTorch (>= 2.0.1)
• PyG (>= 2.3.1)

Dataset

Download the SWU-AMIL dataset and extract it to the folder data.

Evaluation

Download the trained model into the folder trained_models. Then run evaluation:

python test.py experiments/amil.json

Training

Run training:

python train.py experiments/amil.json

Citation

@article{qu2024graph,
 title={Graph Transformer for Label Placement},
 author={Qu, Jingwei and Zhang, Pingshun and Che, Enyu and Chen, Yinan and Ling, Haibin},
 journal={IEEE Transactions on Visualization and Computer Graphics},
 year={2024},
 doi={10.1109/TVCG.2024.3456141}
}