The graph neural network can use the network topology, the attributes and labels of nodes
to mine the potential relationships on network. In paper, we propose a graph convolutional …

Abstract Many studies on Graph Data Augmentation (GDA) approaches have emerged. The
techniques have rapidly improved performance for various graph neural network (GNN) …

Graph neural networks (GNNs) with adaptive propagation combinations represent a
specialized deep learning paradigm, engineered to capture complex nodal interconnections …

Most of the methods in operators' current 5G networks use expert knowledge assisted by
machine learning algorithms to generate optimization decisions. However, these methods …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

Recent advances in data processing have stimulated the demand for learning graphs of very
large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in …

Graph Neural Networks (GNNs) have shown remarkable merit in performing various
learning-based tasks in complex networks. The superior performance of GNNs often …

Generative graph models create instances of graphs that mimic the properties of real-world
networks. Generative models are successful at retaining pairwise associations in the …

Graph generation is integral to various engineering and scientific disciplines. Nevertheless,
existing methodologies tend to overlook the generation of edge attributes. However, we …

This paper investigates the scalability ability of Graph Neural Network (GNN) for solving
resource allocation problems in wireless networks. Although GNNs are able to work on …