MARGIN

Maximal Frequent Subgraph Mining

Dear friends,
This is my optimized implementation of MARGIN.

How to use

Install required packages

conda env create -f environment.yml

Execute algorithm

conda activate margin
python run.py output_file_name memory_log_file_name
                [-h] [--dataset DATASET] [--ckpt CKPT] [--support SUPPORT] [--iso_alg ISO_ALG] [--outdir OUTDIR]
                [--randwalk] [--sortrep] [--confidence CONFIDENCE] [--ngpu NGPU] [--batch_size BATCH_SIZE]
                [--embedding_dim EMBEDDING_DIM] [--n_graph_layer N_GRAPH_LAYER] [--d_graph_layer D_GRAPH_LAYER]
                [--n_FC_layer N_FC_LAYER] [--d_FC_layer D_FC_LAYER] [--initial_mu INITIAL_MU]
                [--initial_dev INITIAL_DEV] [--dropout_rate DROPOUT_RATE]

Get results and memory usage

cat output_file_name
python trace.py memory_log_file_name

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
data_synthesis		data_synthesis
datasets		datasets
model		model
results		results
.gitignore		.gitignore
README.md		README.md
data_structure.py		data_structure.py
environment.yml		environment.yml
gnn.py		gnn.py
gnn_inference.py		gnn_inference.py
layers.py		layers.py
main.py		main.py
margin_gnn.py		margin_gnn.py
margin_vf2.py		margin_vf2.py
run.py		run.py
utils.py		utils.py

martinakaduc/MARGIN

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MARGIN

Maximal Frequent Subgraph Mining

How to use

Install required packages

Execute algorithm

Get results and memory usage

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