LightGFC_

The code of paper " LIGHTWEIGHT GRAPH-FREE CONDENSATION WITH MLPDRIVEN OPTIMIZATION "

Introduction

This repository is the implementation of 2026 ICLR paper: LIGHTWEIGHT GRAPH-FREE CONDENSATION WITH MLPDRIVEN OPTIMIZATION LightGFC condenses large-scale graph data into a structure-free node set in a simple, accurate, yet highly efficient manner.

Our proposed LIGHTGFC contains three essential stages: (S1) Proto-structural aggregation (S2) MLP-driven structural-free pretraining (S3) Lightweight class-to-node condensation.

Requirements

All experiments are implemented in Python 3.9 with Pytorch 1.12.1.

conda env create -f environment.yml

Condensation and Model Training

To condense the graph using LightGFC and train GCN models:

$ python main.py --gpu 0 --dataset reddit --ratio 0.001 

And the result of exps will be saved in ./res/.