chosen for generating waiting time data under different demand and supply scenarios. Graph Convolutional Network (GCN) and …

chosen for generating waiting time data under different demand and supply scenarios. Graph Convolutional Network (GCN) and …

expanding field of study that has attracted much attention recently. Inspired by the graph convolutional network (GCN) approach … graph convolutional network. The weighted average matrix and scaled normalized matrices of low-dimensional vertices have ….39%, 98.79%, and 97.67% for Karate, Cora, CiteSeer, Facebook, and DBLP datasets, respectively. Thus, graph convolutional …

temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional … network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial …

graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared … Markov random fields combined with graph convolutional network (MRFasGCN). Design/methodology/approach This work presents the …