【WSDM-2019 SimGNN】SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
python main.py --exp_name=SimGNN
Parameter | Value |
---|---|
Batch size | 16 |
Bins | 16 |
Bottle neck neurons | 16 |
Dropout | 0.5 |
Epochs | 5 |
Exp name | SimGNN |
Filters 1 | 128 |
Filters 2 | 64 |
Filters 3 | 32 |
Gpu index | 0 |
Histogram | True |
Learning rate | 0.001 |
Seed | 16 |
Tensor neurons | 16 |
Weight decay | 0.0005 |
![Image](https://private-user-images.githubusercontent.com/67092235/323531859-a893764c-81c4-4792-b1f9-fbad70e5e17a.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.BV_DhnSNAJesOUBfNCvdsEhEg6yTw4Ff6Bq6jA87-q8)
Code Framework Reference: SimGNN