XKaiK / 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.