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[AAAI 2025] GraSP: Simple yet Effective Graph Similarity Predictions

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GraSP

Requirements

matplotlib        3.7.1
networkx          2.8.4
numpy             1.23.5
pandas            1.5.3
pyg               2.1.0
pytorch           1.11.0
pytorch-cluster   1.6.0
pytorch-scatter   2.0.9
pytorch-sparse    0.6.15
scikit-learn      1.2.2
scipy             1.10.1
tqdm              4.65.0

Examples on Training and Inference

python src/main.py --gnn rggc --k 8 --epochs 30000 --use-pe --pe-dim 16
python src/main.py --gnn rggc --k 8 --epochs 30000 --use-pe --pe-dim 16 --metric mcs

Reproductivity

To reproduce our empirical results, run the run.sh script.

bash run.sh

Acknowledgement

We thank SimGNN and Extended-SimGNN for providing the core frameworks utilized in this implementation.

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[AAAI 2025] GraSP: Simple yet Effective Graph Similarity Predictions

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