IEEE

Resilient Temporal Graph Convolutional Network for Smart Grid State Estimation Under Topology Inaccuracies

Abstract: Dynamic State Estimation is a crucial task in power systems. Graph Neural Networks have demonstrated significant potential in dynamic state estimation, for power systems by effectively ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

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 ...
IEEE

Graph Tensor Convolutional Network

Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...
Scientific Research Publishing

Xu, Y., Gui, G., Gacanin, H. and Adachi, F. (2021) A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges. IEEE ...

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, ...
EurekAlert!

Real-time, large-scale graph neural network inference through BingoCGN

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...