, players might value some relationships more than others or may have different levels of influence in the graph. These traits … are described by weights on graph edges or vertices, respectively. Relative comparisons on arbitrarily weighted graphs … have been studied for a variety of graphical games. Alternatively, graph labelings are functions that assign values to the …

. We use machine learning and Graph Convolutional Networks (GCNs) to optimize the maintenance schedules for railway …

This study examines the evolution of public debt among the 27 EU member states using Graph Theory tools; the Threshold …

approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique … networks. The heart of our model lies in the integration of graph convolutional networks that capture complex structural …