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Found 4 papers, 1 papers with code
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes
To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories.
Self-Supervised Speech Denoising Using Only Noisy Audio Signals
The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs.
Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding
Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 Hurricane Harvey in Harris County (Texas, USA).
Revealing Critical Characteristics of Mobility Patterns in New York City during the Onset of COVID-19 Pandemic
Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough moment for New York City by aggregating the data at the borough level.
Physics and Society Social and Information Networks
