GSSNN

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

★ 20 Open GitHub ↗

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

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# GSSNN
The implementation of our AAAI 2020 paper "[GSSNN: Graph Smoothing Splines Neural Network](https://www.researchgate.net/publication/337548602_GSSNN_Graph_Smoothing_Splines_Neural_Networks)".

# 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](https://ls11-www.cs.tu-dortmund.de/people/morris/graphkerneldatasets).

### 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](https://github.com/rusty1s/pytorch_geometric) 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}
}
```

GSSNN

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README