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

Python 99.30% Shell 0.70%

rgtn-nie's Introduction

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.

rgtn-nie's People

Contributors

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