Graph Trasformer applicato all’activity prediction su reti di Petri​

This work is a Big Data Analytics and Machine Learning course project.
This project extends the Graph Transformer architecture already developed in a previous study in order to perform activity prediction in graph structured data. Starting from a dataset of graphs representing the execution of a process model, the goal of this work is to predict the next activity of the process.
In order to do so, this work tries to apply the Graph Transformer architecture to the BPI12 dataset.

Requirements

To run our application you need to have installed:

• Python 3.7

• Conda

  # For Linux
  curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
  
  # For OSX
  curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
  

• A set of libraries that you can configure inside a Conda Environment by using the env.yml

  # Install python environment
  conda env create -f env.yml   
  
  # Activate environment
  conda activate graph_transformer
  

Reproduce results

To run our program copy and paste the following command in your terminal:

git clone https://github.com/chiaragii/gtransformers.git
cd gtransformers

python main_BPI_graph_classification.py

You can change the configuration parameters in the GraphsTransformer.json file inside the config folder.

Documentation

In this section you can find the documentation of our project: Documentation

Contributors

Contributor name	Contacts
Gobbi Chiara	chiaragobbi2001@gmail.com
Moretti Alice	morettialice@outlook.it