GRAPH-0 / RGTN-NIE

Implementation for the paper: Representation Learning on Knowledge Graphs for Node Importance Estimation

RGTN-NIE

Dataset and code for Representation Learning on Knowledge Graphs for Node Importance Estimation.

NIE Dataset

• FB15k: a subset from FreeBase.

• TMDB5k: original files are from Kaggle.

• IMDB: original files are from IMDb Datasets. We provide the node text description files on Google Drive, and the graph construction files on Google Drive.

• Processed features: Google Drive. Download the feature files and put them on 'datasets'.

Dependencies

• pytorch 1.6.0
• DGL 0.5.3

Training Examples

• run sh train_geni.sh for GENI in FB15k (full batch training)
• run sh train_geni_batch.sh for GENI in IMDB (minibatch training)
• run sh train_two.sh for RGTN in FB15k (full batch training)
• run sh train_two_batch.sh for RGTN in IMDB (minibatch training)

Note that hyperparameters may require grid search in small datasets.

Citation

If you find our work useful for your reseach, please consider citing this paper:

@inproceedings{Huang21RGTN-NIE,
  author    = {Han Huang and Leilei Sun and Bowen Du and Chuanren Liu and Weifeng Lv and Hui Xiong},
  title     = {Representation Learning on Knowledge Graphs for Node Importance Estimation},
  booktitle = {{KDD} '21: The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021},
  pages     = {646--655},
  publisher = {{ACM}},
  year      = {2021}
}