Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
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MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
citing papers explorer
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Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
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Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
MTGNN with hybrid adjacency matrix reconstructs GRACE-like TWS anomalies to 1940, reaching basin-mean correlation 0.94 and competitive performance with fewer predictors than baselines.
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Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems
MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
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MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
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Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.