GitHub / USC-InfoLab / busyness-graph-neural-network
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.
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PURL: pkg:github/USC-InfoLab/busyness-graph-neural-network
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Forks: 0
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 85.5 MB
Dependencies parsed at: Pending
Created at: over 2 years ago
Updated at: 6 months ago
Pushed at: 6 months ago
Last synced at: 6 months ago
Topics: busyness, busyness-graph, busyness-graph-neural-network, bysgnn, correlations, dynamic-gnns, gnn, gnns, graph-neural-network, graph-neural-networks, machine-learning, ml, poi, point-of-interest, python, sigspatial