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Found 3 papers, 3 papers with code
Distribution Free Prediction Sets for Node Classification
Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many important real world datasets, but provide no rigorous notion of predictive uncertainty.
Time Series Prediction under Distribution Shift using Differentiable Forgetting
Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions.
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study.
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