KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction
Lijing Wang(Hebei University of Science and Technology), Marc Santolini(University of Genoa), Olga Kokshagina(The University of Sydney), Yao Sun(New Jersey Institute of Technology), G. Palomino-Roldán(New Jersey Institute of Technology)
Cited by 2
Related Papers
Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021
|MMWR Morbidity and Mortality Weekly Report|2021|164
Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN
|Medical Physics|2020|117
Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction
|Unknown|2020|109
CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting
|Proceedings of the AAAI Conference on Artificial Intelligence|2022|109