A new study proposes a graph neural network tuned by a modified zebra optimization algorithm that models relationships among ...

A new graph neural network framework called EGNN expands rare attack samples and uses threshold-controlled pseudo-labeling to ...

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

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

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 ...

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 ...