(Wydawnictwo Politechniki Łódzkiej, 2023) Durys, Bartosz; Tomczyk, Arkadiusz
In this paper, the analysis of selected graph neural network operators
is presented. The classic Graph Convolutional Network (GCN) was
compared with methods containing trainable attention coefficients: Graph
Attention Network (GAT) and Graph Transformer (GT). Moreover, which
is an original contribution of this work, training of GT was modified with
an additional loss function component enabling easier explainability of the
produced model. The experiments were conducted using datasets with chemical
molecules where both classification and regression tasks are considered.
The results show that additional constraint not only does not make the results
worse but, in some cases, it improves predictions.
