This is the released codes of the following paper:
Kangzheng Liu, Feng Zhao, Guandong Xu, Xianzhi Wang, and Hai Jin. RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation. ICDE 2023.
Please find the citation information of our paper here:
@inproceedings{DBLP:conf/icde/Liu0X0023,
author = {Kangzheng Liu and
Feng Zhao and
Guandong Xu and
Xianzhi Wang and
Hai Jin},
title = {{RETIA:} Relation-Entity Twin-Interact Aggregation for Temporal Knowledge
Graph Extrapolation},
booktitle = {39th {IEEE} International Conference on Data Engineering, {ICDE} 2023,
Anaheim, CA, USA, April 3-7, 2023},
pages = {1761--1774},
publisher = {{IEEE}},
year = {2023},
url = {https://doi.org/10.1109/ICDE55515.2023.00138},
doi = {10.1109/ICDE55515.2023.00138},
timestamp = {Thu, 27 Jul 2023 17:17:25 +0200},
biburl = {https://dblp.org/rec/conf/icde/Liu0X0023.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}python==3.6.5
torch==1.9.0+cu102
dgl-cu102==0.8.0.post1
tqdm==4.62.3
rdflib==5.0.0
numpy==1.19.5
pandas==1.1.5First, train the model based on the information of an invariant historical range (i.e., the size of the training set). The training parameters for different datasets are presented as follows:
cd src
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7cd src
python main.py -d WIKI --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7cd src
python main.py -d ICEWS14 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 14 --discount 1 --add-static-graphcd src
python main.py -d ICEWS18 --train-history-len 4 --test-history-len 4 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graphcd src
python main.py -d ICEWS05-15 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graphDirectly evaluate the performance of the model obtained by General Training. The testing parameters for different datasets are presented as follows:
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --testpython main.py -d WIKI --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --testpython main.py -d ICEWS14 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 14 --discount 1 --add-static-graph --testpython main.py -d ICEWS18 --train-history-len 4 --test-history-len 4 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --testpython main.py -d ICEWS05-15 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --testThen, continuously train the model based on the newly emerging historical information at the validation or test set timestamps. The online continuous training parameters for different datasets are presented as follow:
Continuously train the model based on the newly emerging history in the validation set:
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --test-validContinuously train and test the model based on the newly emerging history in the test set:
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --test-testContinuously train the model based on the newly emerging history in the validation set:
python main.py -d WIKI --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --test-validContinuously train and test the model based on the newly emerging history in the test set:
python main.py -d WIKI --train-history-len 3 --test-history-len 3 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --test-testContinuously train the model based on the newly emerging history in the validation set:
python main.py -d ICEWS14 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 14 --discount 1 --add-static-graph --test-validContinuously train and test the model based on the newly emerging history in the test set:
python main.py -d ICEWS14 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 14 --discount 1 --add-static-graph --test-testContinuously train the model based on the newly emerging history in the validation set:
python main.py -d ICEWS18 --train-history-len 4 --test-history-len 4 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --test-validContinuously train and test the model based on the newly emerging history in the test set:
python main.py -d ICEWS18 --train-history-len 4 --test-history-len 4 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --test-testContinuously train the model based on the newly emerging history in the validation set:
python main.py -d ICEWS05-15 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --test-validContinuously train and test the model based on the newly emerging history in the test set:
python main.py -d ICEWS05-15 --train-history-len 9 --test-history-len 9 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --gpu 0 --ft_lr=0.001 --norm_weight 1 --task-weight 0.7 --weight 0.5 --angle 10 --discount 1 --add-static-graph --test-testWe provide the general training models for all datasets. The trained models can be downloaded at https://github.com/Liudaxian1/TrainedModels/tree/main/RETIAGeneral_Models. Then, put the trained models into the corresponding folders in the "./models" folder. Note that Github only allows large files to be downloaded one by one (bigger than 25MB). Therefore, go into the last level of each folder in the web to download, to ensure the integrity of the model files.
Contact us with the following email address: FrankLuis@hust.edu.cn.
The source codes take RE-GCN as the backbone to implement our proposed method. Please cite both our work and RE-GCN if you would like to use our source codes.