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

• 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'.

• pytorch 1.6.0
• DGL 0.5.3

• 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.

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}
}