csjtx1021 / neural_ode_processes_for_network_dynamics-masterLinks
Neural ODE Processes for Network Dynamics (NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, is to overcome the fundamental challenge of learning accurate network dynamics with sparse, irregularly-sampled, partial, and noisy observations.
☆13Updated 6 months ago
Alternatives and similar repositories for neural_ode_processes_for_network_dynamics-master
Users that are interested in neural_ode_processes_for_network_dynamics-master are comparing it to the libraries listed below
Sorting:
- Code for the TMLR 2023 paper "GRAM-ODE: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting"☆17Updated last year
- [ECMLPKDD22] MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks☆28Updated 2 years ago
- [KDD 2024] Papers about deep learning in epidemic modeling.☆59Updated 9 months ago
- ☆10Updated last year
- A Python Library for Machine Learning in Epidemic Data Modeling and Analysis☆47Updated 3 months ago
- Paper list on GNNs + Differential Equations (ODE, PDE, SDE)☆30Updated last month
- An awesome collection of causality-inspired graph neural networks.☆80Updated 7 months ago
- Spatial-Temporal Graph ODE Neural Network☆113Updated 3 years ago
- ☆25Updated 3 years ago
- WWW23-Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster☆17Updated 2 years ago
- [ECML-PKDD2022] EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting☆27Updated 2 years ago