VGMGC

This is the code of paper: Variational Graph Generator for Multi-View Graph Clustering.

To cite the paper, please refer:

@misc{chen2022variational,
      title={Variational Graph Generator for Multi-View Graph Clustering}, 
      author={Jianpeng Chen and Yawen Ling and Jie Xu and Yazhou Ren and Shudong Huang and Xiaorong Pu and Zhifeng Hao and Philip S. Yu and Lifang He},
      year={2022},
      eprint={2210.07011},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Requirements

• Python 3.8
• Pytorch 1.11.0
• munkres 1.1.4
• scikit-learn 1.0.1
• scipy 1.8.0

Datasets

ACM and DBLP are included in ./data/. The other datasets are public available.

Dataset	#Clusters	#Nodes	#Features	Graphs
ACM	3	3025	1830	$\mathcal{G}^1$ co-paper $\mathcal{G}^2$ co-subject
DBLP	4	4057	334	$\mathcal{G}^1$ co-author $\mathcal{G}^2$ co-conference $\mathcal{G}^3$ co-term
Amazon photos	8	7487	745 7487	$\mathcal{G}^1$ co-purchase
Amazon computers	10	13381	767 13381	$\mathcal{G}^1$ co-purchase

Test VGMGC

# Test VGMGC on ACM dataset
python vgmgc.py --dataset 'acm' --train False --model_name 'vgmgc_acm.pkl' --order 8 --lam_emd 1

# Test VGMGC on DBLP dataset
python vgmgc.py --dataset 'dblp' --train False --model_name 'vgmgc_dblp.pkl' --order 8 --lam_emd 5

Train VGMGC

# Train VGMGC on ACM dataset
python vgmgc.py --dataset 'acm' --train True --model_name 'vgmgc_acm1.pkl' --order 8 --weight_soft 0.9 --min_belief 0.7 --max_belief 0.99 --lam_emd 1 --kl_step 5 --lam_elbo_kl 1 --threshold 0.8 --temperature 5

# Train VGMGC on DBLP dataset
python vgmgc.py --dataset 'dblp' --train True --model_name 'vgmgc_dblp1.pkl' --order 8 --weight_soft 0.1 --min_belief 0.2 --max_belief 0.99 --lam_emd 5 --kl_step 10 --lam_elbo_kl 1 --threshold 0.8 --temperature 1

Parameters: More parameters and descriptions can be found in the script and paper.

Results of VGMGC

	NMI%	ARI%	ACC%	F1%
ACM	77.3	83.7	94.3	94.3
DBLP	78.3	83.7	93.2	92.7
Amazon photos	66.8	58.4	78.5	76.9
Amazon computers	53.5	47.5	62.2	50.2