Throughout the course of their lives, people typically encounter numerous other individuals with different interests, values ...
Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, ...
Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
Abstract: In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural ...
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 ...
Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...
A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks
Abstract: Port network information security has received extensive attention in recent years, in which the prediction of node links in the network is significant. A Port network is a dynamic network, ...
Positive predictive value was higher with MELD Graph compared with existing baseline algorithm. HealthDay News — A graph neural network using data from the Multicenter Epilepsy Lesion Detection (MELD) ...
Positive predictive value was higher with Multicenter Epilepsy Lesion Detection Graph compared with existing baseline algorithm. HealthDay News — A graph neural network using data from the Multicenter ...
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