A graph convolutional network with incremental learning improves network anomaly detection across three benchmark datasets by ...

A systematic survey of 22 studies finds that no published fraud-detection system simultaneously combines graph learning, edge ...

GTFormer proposes replacing Transformer self-attention with a hierarchical graph neural network, achieving zero-shot length ...

Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a ...

Graph neural networks (GNNs) have emerged as a versatile class of machine-learning models designed to process data structured as graphs, capturing relationships among entities through iterative ...

Fraud attacks can now be assembled with commercially packaged software, distributed across accounts and devices, and altered ...

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 ...

Baldi, meanwhile, is being recognized for his early contributions to the field. He was named the recipient of the 2027 IEEE ...

Graph Neural Networks (GNNs) have emerged as a versatile framework for learning from graph-structured data by propagating and aggregating node and edge features along the topology. Methodological ...