InfoLab, University of Southern California
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busyness-graph-neural-network
busyness-graph-neural-network PublicBusyness Graph Neural Network (BysGNN): A framework for accurate Point-of-Interest visit forecasting using dynamic graphs that capture spatial, temporal, semantic, and taxonomic contexts. Presented…
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- orddc2024 Public
USC-InfoLab/orddc2024’s past year of commit activity - w4h-icde-demo Public
USC-InfoLab/w4h-icde-demo’s past year of commit activity - busyness-graph-neural-network Public
Busyness Graph Neural Network (BysGNN): A framework for accurate Point-of-Interest visit forecasting using dynamic graphs that capture spatial, temporal, semantic, and taxonomic contexts. Presented at ACM SIGSPATIAL 2023, this repository includes code, baselines, and experiments.
USC-InfoLab/busyness-graph-neural-network’s past year of commit activity - w4h-db Public
USC-InfoLab/w4h-db’s past year of commit activity - pyFitbitAPI Public
USC-InfoLab/pyFitbitAPI’s past year of commit activity - NeuroGNN Public
NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, semantic, and taxonomic correlations between EEG electrode locations and brain regions, resulting in improved accuracy. Presented at PAKDD '24.
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