This is the official repository for the paper PUMA: Efficient Continual Graph Learning with Graph Condensation which is an extension of CaT: Balanced Continual Graph Learning with Graph Condensation (code).
The following figure shows the PUMA framework in details.
Our experiments are run on the enviroment based on Python 3.8 with the following packages:
pytorch==1.13.0
torch-geometric==2.2.0 # for deploying GNNs.
ogb==1.3.6 # for obtaining arxiv and prodcuts datasets.
progressbar2==4.2.0 # for visulasing the process of the condensation.
To reproduce the results of Table 2 (classIL setting), please run the table2.sh in the srcripts folder:
run .\srcripts\table2.sh
If you find this repo useful, please cite
@inproceedings{CaT,
author = {Yilun Liu and
Ruihong Qiu and
Yanran Tang and
Hongzhi Yin and
Zi Huang},
title = {PUMA: Efficient Continual Graph Learning with Graph Condensation},
journal = {CoRR},
volume = {abs/2312.14439},
year = {2023}
}