Autoregressive Models for Sequences of Graphs

This is the official implementation of:

Autoregressive Models for Sequences of Graphs.
Daniele Zambon*, Daniele Grattarola*, Lorenzo Livi, Cesare Alippi.
https://arxiv.org/abs/1903.07299
International Joint Conference on Neural Networks (2019).

* Equal contribution

Please cite the paper if you use any of this code for your own research:

@article{zambon2019autoregressive,
  title={Autoregressive Models for Sequences of Graphs},
  author={Zambon, Daniele and Grattarola, Daniele and Livi, Lorenzo and Alippi, Cesare},
  journal={International Joint Conference on Neural Networks},
  year={2019}
}

Abstract

This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) is also proposed to learn the AR function associated with the graph-generating process (GGP), and subsequently predict the next graph in a sequence. The proposed method is compared with four baselines on synthetic GGPs, denoting a significantly better performance on all considered problems.

Setup

The code is implemented in Python 3.5+ and was tested on Ubuntu 16.04.
The following libraries are required to run the code :

• Keras, a high-level API for deep learning;
• Spektral, a library for building graph neural networks with Keras.

Both libraries are available throug PyPi:

pip install keras
pip install spektral

Running experiments

The src folder includes all the necessary code to reproduce the results of the paper.
To test the GNN and baselines, simply run:

$ python src/main_gar.py

There is a section at the top of the script to configure hyperparameters and other experimental details. Check out the comments in the source code for more information.