A PyTorch implementation of the Graph Neural Network Model

This repo contains a PyTorch implementation of the Graph Neural Network model.

The main_simple.py example shows how to use the EN_input format.

Have a look at the Subgraph Matching/Clique detection example, contained in the file main_subgraph.py.

An example of handling the Karate Club dataset can be found in the example main_enkarate.py.

• Website (including documentation): https://mtiezzi.github.io/gnn_site/
• Author: Matteo Tiezzi

Install

Requirements

The GNN framework requires the packages PyTorch, numpy, scipy.

To install the requirements you can use the following command

pip install -U -r requirements.txt

For additional details, please see Install.

Simple usage example

import torch
import utils
import dataloader
from gnn_wrapper import GNNWrapper, SemiSupGNNWrapper

# define GNN configuration
cfg = GNNWrapper.Config()
cfg.use_cuda = use_cuda
cfg.device = device

cfg.activation = nn.Tanh()
cfg.state_transition_hidden_dims = [5,]
cfg.output_function_hidden_dims = [5]
cfg.state_dim = 2
cfg.max_iterations = 50
cfg.convergence_threshold = 0.01
cfg.graph_based = False
cfg.task_type = "semisupervised"
cfg.lrw = 0.001

model = SemiSupGNNWrapper(cfg)
# Provide your own functions to generate input data
E, N, targets, mask_train, mask_test = dataloader.old_load_karate()
dset = dataloader.from_EN_to_GNN(E, N, targets, aggregation_type="sum", sparse_matrix=True)  # generate the dataset

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



#Training

for epoch in range(args.epochs):
    model.train_step(epoch)

Citing

To cite the GNN implementation please use the following publication:

Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini and Marco Gori (2020). "A Lagrangian Approach to Information Propagation in Graph Neural Networks; ECAI2020

Bibtex:

@article{tiezzi2020lagrangian,
  title={A Lagrangian Approach to Information Propagation in Graph Neural Networks},
  author={Tiezzi, Matteo and Marra, Giuseppe and Melacci, Stefano and Maggini, Marco and Gori, Marco},
  journal={arXiv preprint arXiv:2002.07684},
  year={2020}
}

License

Released under the 3-Clause BSD license (see LICENSE.txt):

Copyright (C) 2004-2020 Matteo Tiezzi
Matteo Tiezzi <[email protected]>