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Found 5 papers, 2 papers with code
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting
Unlike typical GNN methods that treat each traffic sensor as an individual graph node, DeepStateGNN clusters sensors into higher-level graph nodes, dubbed Deep State Nodes, based on various similarity criteria, resulting in a fixed number of nodes in a Deep State graph.
WaveGNN: Modeling Irregular Multivariate Time Series for Accurate Predictions
Accurately modeling and analyzing time series data is crucial for downstream applications across various fields, including healthcare, finance, astronomy, and epidemiology.
Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions.
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
