Abstract: This paper introduces a novel dynamic graph learning approach for frequency graphs, underpinned by a suite of baseline methodologies and the Multi-scale Controllable Graph Convolutional ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Abstract: This paper proposes a spatio-temporal graph convolutional network incorporating knowledge graph embeddings for hydrological time series prediction. A knowledge graph is constructed to ...
ABSTRACT: The study aims to provide insights into the benefits and potential risks associated with its adoption. The findings will be valuable for organizations considering transitioning to SDN, ...