VGATM

This is the tensorflow implementation of KDD-2022 paper "Variational Graph Author Topic Modeling" by Delvin Ce Zhang and Hady W. Lauw.

VGATM is a Grapn Neural Network model that extracts interpretable topics for documents with authors and venues. Topics of documents then fulfill document classification, citation prediction, etc.

Implementation Environment

• Python == 3.6
• tensorflow == 1.9.0
• numpy == 1.17.4
• sklearn == 0.23.2
• scipy == 1.5.2

Run

• python main.py -div kl -p gaussian # VGATM-G (unsupervised)

• python main.py -div kl -p dirichlet # VGATM-D (unsupervised)

• python main.py -div wasserstein -p gaussian # VGATM-W (unsupervised)

• python main.py -div kl -p gaussian -sup 1 # VGATM-G (supervised)

• python main.py -div kl -p dirichlet -sup 1 # VGATM-D (supervised)

• python main.py -div wasserstein -p gaussian -sup 1 # VGATM-W (supervised)

Parameter Setting

• -ne: number of training epochs, default=15
• -lr: learning rate, default=0.01
• -ms: minibatch size, default=128
• -dn: dataset name, ml or pl, default=ml
• -nt: number of topics, default=64
• -sup: label supervision, default=0 (no supervision)
• -tr: training ratio of documents, default=0.8
• -nn: number of sampled neighbours for convolution, default=5
• -ws: word-word graph sliding window size, default=5
• -wn: word-word graph number of neighbours for each word, default=5
• -nl: number of convolutional steps L, default=2
• -div: variational divergence metric R, kl or wasserstein, default=wasserstein
• -p: predefined prior distribution p(.), gaussian or dirichlet, default=gaussian
• -reg_div: hyperparameter \lambda_reg controlling variational divergence term, default=0.01
• -reg_l2: hyperparameter for l2 regularization, default=1e-3
• -kp: dropout keep probability, default=0.9
• -ap: author prediction, default=0 (no author prediction)
• -rs: random seed
• -gpu: gpu

Output

Results will be saved to ./results file.

• doc_topic_dist_training.txt contains topic proportions of training documents.
• doc_topic_dist_test.txt contains topic proportions of test documents.
• word_topic_dist.txt contains topic proportions of words.
• author_topic_dist.txt contains topic proportions of authors.

Reference

If you use our paper, including code and data, please cite

@inproceedings{vgatm,
  title={Variational Graph Author Topic Modeling},
  author={Zhang, Delvin Ce and Lauw, Hady W},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={2429--2438},
  year={2022}
}