ClustGDD

The code for paper "Simple yet Effective Graph Distillation via Clustering" published at KDD 2025

Brief Introduction

This paper proposes a simple yet effective approach ClustGDD for graph data distillation. ClustGDD achieves superior performance in condensation effectiveness and efficiency over previous GDD solutions on real datasets through two major contributions: a simple clustering method minimizing the WCSS and a lightweight module augmenting synthetic attributes with class-relevant features.

environment

numpy                     1.26.0
matplotlib                3.8.2
ogb                       1.3.6 
python                    3.11.5
scikit-learn              1.3.2
torch                     2.0.1                  
torch-cluster             1.6.1+pt20cu118          
torch-geometric           2.6.1                   
torch-scatter             2.1.2+pt20cu118         
torch-sparse              0.6.17+pt20cu118       
torch-spline-conv         1.2.2+pt20cu118

PS: deep_robust_data.py and deep_robust_uitls.py are two scource code deeprobust.graph.data and deeprobust.graph.utils from repo deeprobust, you can install it by

pip install deeprobust

and see the code.

Datasets

See the datasets used in https://github.com/ChandlerBang/GCond

Runs

sh main_induct.sh
sh main_transduct.sh

Reference

https://github.com/ChandlerBang/GCond

https://github.com/Amanda-Zheng/SFGC

https://github.com/zclzcl0223/GCSR

https://github.com/liuyang-tian/GDEM