Graph Spatial Information Preservation (GSIP)

This is the official repository for the paper What Matters in Graph Class Incremental Learning? An Information Preservation Perspective (NeurIPS 2024).

Get Started

This repository contains our GSIP implemented for running on GPU devices. To run the code, the following packages are required to be installed:

• python==3.7.10
• scipy==1.5.2
• numpy==1.19.1
• torch==1.7.1
• networkx==2.5
• scikit-learn~=0.23.2
• matplotlib==3.4.1
• ogb==1.3.1
• dgl==0.6.1
• dgllife==0.2.6

Usage

Below is the example to run the ERGNN baseline with GCN backbone on the CoraFull-CL and Reddit-CL datasets under the class-IL scenario.

 python train.py --dataset CoraFull-CL \
        --n_base 2 \ 
        --n_cls_per_task 2 \ 
        --ergnn_args="'budget':[100];'d':[0.5];'sampler':['CM'];'w_ll':[50];'w_lg':[0.05];'w_h':[10]" \
        --neibt1=0.5 \ 
        --method ergnn \
        --backbone GCN \
        --gpu 0 \
        --ILmode classIL \
        --inter-task-edges False \

python train.py --dataset Reddit-CL \
       --n_base 10 \ 
       --n_cls_per_task 5 \ 
       --ergnn_args="'budget':[100];'d':[0.5];'sampler':['CM'];'w_ll':[1];'w_lg':[1e-3];'w_h':[5e-6]" \
       --neibt1=0.9 \ 
       --method ergnn \
       --backbone GCN \
       --gpu 0 \
       --ILmode classIL \
       --inter-task-edges False \

Cite

If you find this repository helpful, please click the ⭐Star and cite our paper:

@inproceedings{GSIP,
  author    = {Jialu Li and Yu Wang and Pengfei Zhu and Wanyu Lin and Qinghua Hu},
  title     = {What Matters in Graph Class Incremental Learning? An Information Preservation Perspective},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2024}
  pages     = {1-14},
}

Credit

This repository was developed based on the CGLB and CaT.