CoSimGNN: Coarsening-based Similarity Computation via Graph Neural Networks

This repository is the official implementation of CoSimGNN: Towards Large-scale Graph Similarity Computation.

Requirements

To install requirements:

Using pip:
	pip install -r requirements_pip.txt

Using conda:
	conda create -n your_env_name --file requirements_conda.txt

Training

To train the model in the paper, run this command:

python main.py --dataset <dataset> --num_iters <number_of_training_iterations> --model <model_name> --dos_true 'dist'

<dataset> can be among {BA_60, BA_100, BA_200, ER_100, IMDB-L}.

<model_name> can be among {GCN-Mean, GCN-Max, simgnn, gsim_cnn, GMN, CoSim-CNN, CoSim-Mem, CoSim-ATT, CoSim-SAG, CoSim-TOPK, CoSim-GNN10, CoSim-GNN1}

Evaluation

To evaluate the models, run:

python main.py --dataset <dataset> --num_iters <number_of_training_iterations> --model <model_name> --dos_true 'dist' --load_model <model.pth>

Pre-trained Models

Download pretrained models here:

• Pre-trained_models

Results

The results are shown as below:

model	BA-60		BA-100		BA-200		ER-100		IMDB-L
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
HUNGARIAN	186.19	332.2	205.83	343.83	259.06	379.38	236.15	451.66	2.67	16.6
VJ	258.73	294.83	273.94	404.61	314.45	426.86	275.22	463.53	7.68	22.73
BEAM	59.14	129.26	114.02	203.76	186.03	287.88	104.73	226.42	0.39	3.93
GCN-MEAN	5.85	53.92	12.53	90.88	23.66	127.58	16.58	92.98	22.17	55.35
GCN-MAX	13.66	91.38	12.04	85.44	22.77	107.58	79.09	211.07	47.14	123.16
SIMGNN	8.57	63.8	6.23	47.8	3.06	32.77	6.37	45.3	7.42	33.74
GSIMCNN	5.97	56.05	1.86	30.18	2.35	32.64	2.93	34.41	5.01	30.43
GMN	2.82	38.38	4.14	34.17	1.16	26.6	1.59	28.68	3.82	27.28
COSIM-CNN	2.5	35.53	1.49	27.2	0.53	18.44	2.78	33.36	10.37	38.03
COSIM-ATT	2.04	33.41	0.97	22.95	0.73	16.26	1.39	27.27	1.53	16.57
COSIM-SAG	3.26	38.85	3.3	33.14	1.91	35.48	1.55	29.84	1.62	16.08
COSIM-TOPK	3.44	40.87	1.24	25.61	0.88	20.63	2.04	34.28	1.98	20.02
COSIM-MEM	5.45	48.07	1.11	24.59	0.32	14.82	1.74	26.78	1.57	17.02
COSIM-GNN10	2.04	33.04	1.01	23.53	0.40	16.43	1.38	27.43	1.68	17.57
COSIM-GNN1	1.84	32.36	0.95	22.06	0.36	15.42	1.17	25.73	2.00	18.62

To reproduce the results, just download the trained models and set <model.pth> below to any trained models trained under specific dataset. The code will automatically extract all the trained models and validate them on the validation set to choose the best one on the validation set for testing.

python main.py --dataset <dataset> --num_iters <number_of_training_iterations> --model <model_name> --dos_true 'dist' --load_model <model.pth>

Contributing

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