F2GNN -- Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective

This repository is the code for our WWW 2022 paper: Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective

Overview

In this paper, we provide a novel feature fusion perspective in designing the GNN topology. Firstly, we propose a novel framework to unify the existing topology designs with feature selection and fusion strategies. It transforms the GNN topology design into the design of this two strategies. Then we develop a NAS method on top of the unified framework containing a novel search space and an improved differentiable search algorithm. Extensive experiments on eight real-world datasets demonstrate that the proposed F2GNN can improve performance while alleviating the deficiencies, especially alleviating the over-smoothing problem.

Requirements

 torch-cluster==1.5.7
 torch-geometric==1.7.2
 torch-scatter==2.0.6
 torch==1.6.0
 numpy==1.17.2
 hyperopt==0.2.5
 python==3.7.4

Instructions to run the experiment

Step 1. Run the search process, given different random seeds. (The wisconsin dataset is used as an example)

(F2SAGE) python train_search.py --data wisconsin   --gpu 0  --agg sage --temp 0.001 --arch_learning_rate 0.01 --epochs 400  --learning_rate 0.02  

(F2GAT)  python train_search.py --data wisconsin   --gpu 0  --agg gat  --temp 0.001 --arch_learning_rate 0.01 --epochs 400  --learning_rate 0.02    

(F2GNN)  python train_search.py --data wisconsin   --gpu 0  --search_agg True --temp 0.001 --arch_learning_rate 0.01 --epochs 400  --learning_rate 0.02    

(Random SAGE) python train_search.py --data wisconsin   --gpu 0  --agg sage   --algo random --temp 0.001 --arch_learning_rate 0.01 --epochs 400  --learning_rate 0.02  --alpha_mode train --random_epoch 100

The results are saved in the directory exp_res, e.g., exp_res/wisconsin_sage.txt.

Step 2. Fine tune the searched architectures. You need specify the arch_filename with the resulting filename from Step 1.

python fine_tune.py --data wisconsin --gpu 0  --hyper_epoch 30  --arch_filename  exp_res/wisconsin_sage.txt --cos_lr --layer_norm True 

Step 2 is a coarse-graind tuning process, and the results are saved in a picklefile in the directory tuned_res, e.g., tuned_res/wisconsin_sage.pkl.

Cite

Please kindly cite our paper if you use this code:

@inproceedings{wei2021designing,  
title={Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective},  
author={Wei, Lanning and Zhao, Huan and He, Zhiqiang},  
journal={WebConf},  
year={2022}  
}