Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs

This is a TensorFlow implementation of DSGC (a 2-D graph filtering framework) for semi-supervised and unsupervised classification proposed in our paper:

• Qimai LI, Xiaotong Zhang, Han Liu, Quanyu Dai, and Xiao-Ming Wu. 2021. Dimensionwise Separable 2-D Graph Convolution for Unsupervisedand Semi-Supervised Learning on Graphs. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore. ACM, New York, NY, USA, 12 pages.

Requirements

Our code is tested with packages of following version:

• Python=3.7
• TensorFlow=1.14
• numpy=1.18.1
• cupy=7.5.0 (optional)

Download Dataset

Go to https://www.dropbox.com/s/r4btgq1djubgfwt/DSGC-KDD21.zip?dl=0, download all files in DSGC-KDD21/data/ directory and put them into your local data/ directory.

Reproduce Experimental Results

Classification

Run train.py to reproduce classification results. Edit config.py to change settings, models, and parameters. You can set 'repeat' to 1 for fast try.

# 20 labels/class
python train.py --seed 0 --XF --GXF --train-size 20 --weight-decay 5e-5 --repeat 50 --dataset large_cora
python train.py --seed 0 --XF --GXF --train-size 20 --weight-decay 5e-6 --repeat 50 --dataset 20news
python train.py --seed 0 --XF --GXF --train-size 20 --weight-decay 5e-7 --repeat 50 --dataset wiki

# 5 labels/class
python train.py --seed 0 --XF --GXF --train-size 5  --weight-decay 5e-5 --repeat 50 --dataset large_cora
python train.py --seed 0 --XF --GXF --train-size 5  --weight-decay 5e-6 --repeat 50 --dataset 20news
python train.py --seed 0 --XF --GXF --train-size 5  --weight-decay 5e-7 --repeat 50 --dataset wiki

# WebKB
python train.py --seed 0 --GCN --XF --GXF --train-size 0.05 --valid-size 0.3 --repeat 50 --dataset webkb_cornell
python train.py --seed 0 --GCN --XF --GXF --train-size 0.05 --valid-size 0.3 --repeat 50 --dataset webkb_texas
python train.py --seed 0 --GCN --XF --GXF --train-size 0.05 --valid-size 0.3 --repeat 50 --dataset webkb_wisconsin
python train.py --seed 0 --GCN --XF --GXF --train-size 0.05 --valid-size 0.3 --repeat 50 --dataset webkb_washington

Clustering

Run clustering.py to reproduce clustering results of 20 Newgroups. Edit clustering.py to change dataset.

python clustering.py

Acknowledgement

This project is largely based on Thomas N. Kipf's GCN code. Thanks kipf for making code public. The source of dataset is listed here:

• Cora: Full 40k+ Paper Dataset can be found here. The 11881 paper subset we used was selected by Marco Gori. We did not use features he provided, but process raw papers by ourselves instead.
• Wikispeedia: https://snap.stanford.edu/data/wikispeedia.html.
• 20 Newgroups: http://qwone.com/~jason/20Newsgroups/
• WebKB: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/

Thanks them for providing first-hand data.

Citation

If your work relies on this repository or uses the dataset we provided, please cite the following paper.

@inproceedings{li2021dimensionwise,
      author    = "Li, Qimai and Zhang, Xiaotong and Liu, Han and Dai, Quanyu and Wu, Xiao-Ming",
      title     = "Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs",
      booktitle = "Proceedings of *the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21)",
      year      = 2021,
      publisher = "ACM",
      address   = "New York, NY, USA"
    }