Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck（BiMSGC）

This is the official code for AAAI 2025 Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck

Requirements

deeprobust==0.2.9
gdown==4.7.3
networkx==3.2.1
numpy==1.26.3
ogb==1.3.6
pandas==2.1.4
scikit-learn==1.3.2
scipy==1.11.4
torch==2.1.2
torch_geometric==2.4.0
torch-sparse==0.6.18

Download Datasets

For Citeseer Pubmed and Squirrel, the code will directly download them. For Reddit, Flickr, and Ogbn-arXiv, we use the datasets provided by GraphSAINT. They are available on Google Drive link (the links are provided by GraphSAINT team). Download the files and unzip them to data at the root directory.

Instructions

(1) Run preprocess.py to preprocess the dataset and conduct the spectral decomposition.

(2) Initialize node features of the synthetic graph by running feat_init.py.

(3) Distill the synthetic graph by running distill.py.

Cite

Welcome to kindly cite our work!