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ERIC

[NeurIPS-2022] Efficient Graph Similarity Computation with Alignment Regularization

The final version of the code is to be further cleaned.

Requirements

tqdm==4.62.3
numpy==1.20.3
colour==0.1.5
seaborn==0.11.2
torch_scatter==2.0.9
networkx==2.6.3
torch_sparse==0.6.12
matplotlib==3.4.3
torch==1.10.0
gpustat==0.6.0
torch_geometric==2.0.2
scipy==1.7.2
PyYAML==6.0
rich==12.4.1
scikit_learn==1.1.0
pygraphviz
pytz

Run Pretrained Model

python main.py --dataset AIDS700nef --run_pretrain --pretrain_path model_saved/AIDS700nef/2022-03-17_10-42-12

python main.py --dataset LINUX --run_pretrain --pretrain_path model_saved/LINUX/2022-03-20_03-01-57

Running Example

image

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eric's Issues

Question about the Loss term "L_AReg"

  1. Thanks for your excellent work. I have question that it seems the computation way of "L_AReg" in the paper is different from that in the code, in the paper, L_AReg = r1 + r2 + ||r1-r2||_2 while in the codes, it changes to r1+r2 or r1-r2, Could you please explain this issue? Very glad to receive your reply.

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