TiRGN
This is the code of TiRGN.
Installation
conda create -n tirgn python=3.7
conda activate tirgn
pip install -r requirement.txt
How to run
Process data
For all the datasets, the following command can be used to get the history of their entities and relations.
cd src
python get_history.py --dataset ICEWS14
Train models
Then the following commands can be used to train TiRGN.
Train models
python main.py -d ICEWS14 --history-rate 0.3 --train-history-len 9 --test-history-len 9 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 14 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint
Evaluate models
The following commands can be used to evaluate TiRGN (add --test only).
Test with ground truth history:
python main.py -d ICEWS14 --history-rate 0.3 --train-history-len 9 --test-history-len 9 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 14 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
Test without ground truth history:
python main.py -d ICEWS14 --history-rate 0.3 --train-history-len 9 --test-history-len 9 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 14 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test --multi-step --topk 0
Detailed hyperparameters
The following commands and trained models (google drive) can be used to get the entity prediction results reported in the paper (remove --test to train new models).
ICEWS14
python main.py -d ICEWS14 --history-rate 0.3 --train-history-len 9 --test-history-len 9 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 14 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
ICEWS14s
python main.py -d ICEWS14s --history-rate 0.3 --train-history-len 9 --test-history-len 9 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 14 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
ICEWS18
python main.py -d ICEWS18 --history-rate 0.3 --train-history-len 10 --test-history-len 10 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
ICEWS05-15
python main.py -d ICEWS05-15 --history-rate 0.3 --train-history-len 15 --test-history-len 15 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
WIKI
python main.py -d WIKI --history-rate 0.3 --train-history-len 2 --test-history-len 2 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
YAGO
python main.py -d YAGO --history-rate 0.3 --train-history-len 1 --test-history-len 1 --dilate-len 1 --lr 0.001 --n-layers 1 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
GDELT
python main.py -d GDELT --history-rate 0.3 --train-history-len 7 --test-history-len 7 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder timeconvtranse --encoder convgcn --layer-norm --weight 0.5 --entity-prediction --relation-prediction --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --save checkpoint --test
Cite
Please cite as:
@inproceedings{DBLP:conf/ijcai/LiS022,
author = {Yujia Li and Shiliang Sun and Jing Zhao},
title = {TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning},
booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI} 2022, Vienna, Austria, 23-29 July 2022},
pages = {2152--2158},
year = {2022},
}