Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning

This is the official code release of the following paper:

Hao Dong, Zhiyuan Ning, Pengyang Wang, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou and Yanjie Fu. "Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning." IJCAI 2023.

Quick Start

Dependencies

python==3.8
torch==1.10.0
torchvision==0.11.1
dgl-cu113==0.9.1
tqdm
torch-scatter>=2.0.8
pyg==2.0.4

Train models

0. Switch to src/ folder

cd src/

1. Run scripts

python main.py --gpus 0 -d YAGO --batch_size 64 --history_len 10

• To run with multiple GPUs which is highly recommended, use the following commands

python -m torch.distributed.launch --nproc_per_node=4 main.py --gpus 0 1 2 3 -d YAGO --batch_size 16 --history_len 10

Evaluate models

To generate the evaluation results of a pre-trained model (if exist), simply add the --test flag in the commands above.

python main.py --gpus 0 -d YAGO --batch_size 64 --history_len 10 --test

Change the hyperparameters

To get the optimal result reported in the paper, change the hyperparameters and other setting according to the details of Section 5.1 in the paper.

Citation

If you find the resource in this repository helpful, please cite

@article{dong2023adaptive,
  title={Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning},
  author={Dong, Hao and Ning, Zhiyuan and Wang, Pengyang and Qiao, Ziyue and Wang, Pengfei and Zhou, Yuanchun and Fu, Yanjie},
  journal={arXiv preprint arXiv:2304.12604},
  year={2023}
}