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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