pip install -U -r requirements.txt

pip install gnn

import gnn.GNN as GNN
import gnn.gnn_utils
import Net as n

# Provide your own functions to generate input data
inp, arcnode, nodegraph, labels = set_load()

# Create the state transition function, output function, loss function and  metrics 
net = n.Net(input_dim, state_dim, output_dim)

# Create the graph neural network model
g = GNN.GNN(net, input_dim, output_dim, state_dim)

#Training

for j in range(0, num_epoch):
    g.Train(inp, arcnode, labels, count, nodegraph)

    # Validate            
    print(g.Validate(inp_val, arcnode_val, labels_val, count, nodegraph_val))

Rossi, A., Tiezzi, M., Dimitri, G.M., Bianchini, M., Maggini, M., & Scarselli, F. (2018).
"Inductive–Transductive Learning with Graph Neural Networks", 
In Artificial Neural Networks in Pattern Recognition (pp.201-212). 
Berlin : Springer-Verlag.

@inproceedings{rossi2018inductive,
  title={Inductive--Transductive Learning with Graph Neural Networks},
  author={Rossi, Alberto and Tiezzi, Matteo and Dimitri, Giovanna Maria and Bianchini, Monica and Maggini, Marco and Scarselli, Franco},
  booktitle={IAPR Workshop on Artificial Neural Networks in Pattern Recognition},
  pages={201--212},
  year={2018},
  organization={Springer}
}

F. Scarselli, M. Gori,  A. C. Tsoi, M. Hagenbuchner, G. Monfardini, 
"The Graph Neural Network Model", IEEE Transactions on Neural Networks,  
vol. 20(1); p. 61-80, 2009.

@article{Scarselli2009TheGN,
  title={The Graph Neural Network Model},
  author={Franco Scarselli and Marco Gori and Ah Chung Tsoi and Markus Hagenbuchner and Gabriele Monfardini},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={61-80}
}

Copyright (C) 2004-2019 Matteo Tiezzi
Matteo Tiezzi <[email protected]>
Alberto Rossi <[email protected]>