Scholars deliver the first systematic survey of Dynamic GNNs, unifying continuous- and discrete-time models, benchmarking ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...
Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances
Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
Abstract: With the rapid development of mobile internet in recent years, a large scale of continuous arrival correlative data, namely dynamic streaming graph, are extensively generated in various ...
Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...
ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...
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