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Proceedings Paper

Implementation aspects of Graph Neural Networks
Author(s): A. Barcz; Z. Szymański; S. Jankowski
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Paper Abstract

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.

Paper Details

Date Published: 25 October 2013
PDF: 9 pages
Proc. SPIE 8903, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2013, 89032S (25 October 2013); doi: 10.1117/12.2035443
Show Author Affiliations
A. Barcz, Warsaw Univ. of Technology (Poland)
Z. Szymański, Warsaw Univ. of Technology (Poland)
S. Jankowski, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 8903:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2013
Ryszard S. Romaniuk, Editor(s)

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