GSSNN

The implementation of our AAAI 2020 paper "GSSNN: Graph Smoothing Splines Neural Network".

Requirements

python == 3.6.2
torch == 1.1.0
numpy == 1.16.4
scipy == 1.2.0
networkx == 2.2
torch_scatter == 1.3.0
torch_geometric == 1.3.0

How to use

Dataset

The data folder includes our propocessed data for training and testing.
The orginal datasets can be founded from here.

Model

The model folder includes our proposed model "GSSNN".
The model/utils folder includes graph utils and Scaled Smoothing Splines module used in model.
The model/process_data file processes data and computes the graph centrality.
The torch_geometeric/nn/pool folder includes the designed NodeImportance layer used in model.
The torch_geometeric/nn/conv folder includes the convolutional layers used in model provides by torch_geometeric library.

Prameters Setting

dim: the hidden dimension of node feature
conv_layer: the number of convolutional layer
ss_layer: the number of smoothing splines layer
Mi: the number of knot used in smoothing splines layer i
epsilon: used in smoothing splines to guarantee the denominator non-zero
add_knot: whether to consider the important nodes features as residual connection to the graph-level representation

Training/Testing

cd model
python process_data.py --dataset MUTAG
python GSSNN.py --dataset MUTAG --batch_size 128 --lr 0.01 --weight_decay 5e-4 --dim 32 --conv_layer 3 --ss_layer 2 --M1 5 --M2 5 --epsilon 1e-6 --add_knot True --epoch 100
python GSSNN_10_folds.py --dataset MUTAG --batch_size 128 --lr 0.01 --weight_decay 5e-4 --dim 32 --conv_layer 3 --ss_layer 2 --M1 5 --M2 5 --epsilon 1e-6 --add_knot True --epoch 100

Citation

@inproceedings{zhu2020GSSNN
author={Shichao Zhu and Lewei Zhou and Shirui Pan and Chuan Zhou and Guiying Yan and Bin Wang},
title={GSSNN: Graph Smoothing Splines Neural Network},
journal={Proceedings of the 34th AAAI Conference on Artificial Intelligence},
year={2020}
}