Our research group introduced the Graph Neural Network (GNN), a connectionist model particularly suited for problems whose domain can be represented by a set of patterns and relationships between them.
In those problems, a prediction about a given pattern can be carried out exploiting all the related information, which includes the pattern features, the pattern relationships and, in general, the whole graph that represents the domain. GNN peculiarity consists in its capability of computing the output prediction processing directly the input domain graph, without any preprocessing into a vectorial representation.
GNNs have been proved to be a universal approximator for a class of functions on graphs and have been applied to several tasks, including spam detection, object localization in images, molecule classification.
The GNN was originally implemented in MATLAB but nowadays frameworks such as Tensorflow are more popular in the machine learning community.
We provide an implementation of the model developed under the supervision of the authors of the original work.
You can find a description of the model, installation/usage tutorials and some examples in the Gnn site.
We provide a github repo containing our implementation and a pip package to easy intall our software.