PUMA (Continual Graph Learning)

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.

Main experiment results.

Experiment environment

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.

Usage

To reproduce the results of Table 2 (classIL setting), please run the table2.sh in the srcripts folder:

bash ./scripts/table2.sh

Cite

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}
}

Credit

This repository was developed based on the CGLB and CaT.