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cs.LG2023
Tango: rethinking quantization for graph neural network training on GPUs
Shiyang Chen, Da Zheng, Caiwen Ding +3
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in critical graph-related tasks. While quantization is widely used to accelerate GN…
cs.LG2023★ 1 cited
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis
Haoyu He, Yuede Ji, H. Howie Huang
Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysi…