, 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 …

connections that can be efficiently modeled using graph neural networks (GNNs). The key mechanism of GNNs is the message passing … features from its neighbors and combining them with its own. In convolutional graph neural networks (GCNs), all neighboring … importance of neighbors, graph attention networks (GAT) have been introduced. The aim of the study was to evaluate the …

roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow …

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 …

proposes a method based on Graph Clustering Neural Networks to analyze and study urban functional areas. To begin with, the …-economic characteristics. Through the use of Graph Clustering Neural Networks and clustering algorithms, urban functional areas can be … identified through clustering through the integration of Graph Neural Networks and clustering algorithms. For functional area …