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Repository Details

Multi-relational Poincaré Graph Embeddings

Multi-relational Poincaré Graph Embeddings

Multi-relational link prediction in the Poincaré ball model of hyperbolic space.

This codebase contains PyTorch implementation of the paper:

Multi-relational Poincaré Graph Embeddings. Ivana Balažević, Carl Allen, and Timothy M. Hospedales. Neural Information Processing Systems (NeurIPS), 2019. [Paper]

Link Prediction Results

Model Dataset dim MRR Hits@10 Hits@3 Hits@1
MuRP WN18RR 40 0.477 0.555 0.489 0.438
MuRP WN18RR 200 0.481 0.566 0.495 0.440
MuRE WN18RR 40 0.459 0.528 0.474 0.429
MuRE WN18RR 200 0.475 0.554 0.487 0.436
MuRP FB15k-237 40 0.324 0.506 0.356 0.235
MuRP FB15k-237 200 0.335 0.518 0.367 0.243
MuRE FB15k-237 40 0.315 0.493 0.346 0.227
MuRE FB15k-237 200 0.336 0.521 0.370 0.245

Running a model

To run the model, execute the following command:

 CUDA_VISIBLE_DEVICES=0 python main.py --model poincare --dataset WN18RR --num_iterations 500 
                                       --nneg 50 --batch_size 128 --lr 50 --dim 40 

Available datasets are:

FB15k-237
WN18RR

To reproduce the results from the paper, use learning rate 50 for WN18RR and learning rate 10 for FB15k-237.

Requirements

The codebase is implemented in Python 3.6.6. Required packages are:

numpy      1.15.1
pytorch    1.0.1

Citation

If you found this codebase useful, please cite:

@inproceedings{balazevic2019multi,
title={Multi-relational Poincar$\backslash$'e Graph Embeddings},
author={Bala{\v{z}}evi{\'c}, Ivana and Allen, Carl and Hospedales, Timothy},
booktitle={Advances in Neural Information Processing Systems},
year={2019}
}