Parameter estimation of epidemic spread in two-layer random graphs by classical and machine learning methods

Our main goal in this paper is to quantitatively compare the performance of classical methods to XGBoost and convolutional neural networks in a parameter estimation problem for epidemic spread. As we use flexible two-layer random graphs as the underlying network, we can also study how much the struc...

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Hauptverfasser:	Backhausz, Ágnes, Bognár, Edit, Csiszár, Villő, Tárkányi, Damján, Zempléni, András
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Social and Information Networks Quantitative Biology - Populations and Evolution
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