Implementation aspects of Graph Neural Networks
A. Barcz, Zbigniew Szymanski, S. Jankowski · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
This article summarises the results of implementation of a Graph Neural Network classi er. The Graph Neural Network model is a connectionist model, capable of processing various types of structured data, including non- positional and cyclic graphs. In order to operate correctly, the GNN model must implement a transition function being a contraction map, which is assured by imposing a penalty on model weights. This article presents research results concerning the impact of the penalty parameter on the model training process and the practical decisions that were made during the GNN implementation process.