Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
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 ...
IEEE

DAHBM-GCN: A Flexible Graph Convolution Network Accelerator With Multiple Dataflows and HBM

Abstract: Graph-structured data has been widely applied in transportation, molecular, and e-commerce networks, etc. Graph Convolutional Network (GCN) has emerged as an efficient approach to processing ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...