Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning

This is the official code release of the following paper:

Hao Dong, Pengyang Wang, Meng Xiao, Zhiyuan Ning, Pengfei Wang and Yuanchun Zhou. "Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning." Artificial Intelligence 2024.

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 32 --n_epoch 20 --lr 0.0001 --hidden_dims 64 64 64 64 --history_len 8 --time_encoding_independent

• 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 32 --n_epoch 20 --lr 0.0001 --hidden_dims 64 64 64 64 --history_len 8 --time_encoding_independent

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 32 --hidden_dims 64 64 64 64 --history_len 8 --time_encoding_independent --test

Inductive setting

The dataset of inductive setting is also located at data/, namely YAGO(1to6). Note that YAGO1-YAGO2, YAGO3-YAGO4, YAGO5-YAGO6 correspond to the three different dataset pairs created with different entity set partition ratios. The statistics and other details can be found in the paper.

Take YAGO1-YAGO2 as an example, to run the inductive prediction, one can train on the training set of YAGO1 and test on the test set of YAGO2, and vice versa.

python inductive.py --gpus 0 -d YAGO2 --batch_size 32 --hidden_dims 64 64 64 64 --history_len 8 --time_encoding_independent --pretrain_name YAGO1

Note that one should ensure the pre-trained model is relocated to the model folder of target dataset.

Change the hyperparameters

To get the optimal result reported in the paper, change the hyperparameters and other setting according to the Implementation Details in the paper.

Citation

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

@article{dong2024temporal,
  title={Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning},
  author={Dong, Hao and Wang, Pengyang and Xiao, Meng and Ning, Zhiyuan and Wang, Pengfei and Zhou, Yuanchun},
  journal={Artificial Intelligence},
  pages={104085},
  year={2024},
  publisher={Elsevier}
}