MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting , url=
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4representative citing papers
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.
The paper provides a structured overview of IoE concepts, components, architectures, enabling technologies, challenges, and open research directions for 6G-enabled systems.
citing papers explorer
-
Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
-
Incident-Guided Spatiotemporal Traffic Forecasting
IGSTGNN adds incident-context spatial fusion and temporal impact decay modules to model how events alter traffic patterns, achieving state-of-the-art results on a new time-aligned incident-traffic dataset.
-
The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
The Time-Geometric model combines GNNs for geometric patterns with temporal models and reports statistically significant accuracy gains in financial time series forecasting.
-
Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions
The paper provides a structured overview of IoE concepts, components, architectures, enabling technologies, challenges, and open research directions for 6G-enabled systems.