Ed Davis

2 accepted papers

2025

Valid Bootstraps for Network Embeddings with Applications to Network Visualisation

Emerald Dilworth, Ed Davis, Daniel John Lawson

UAI 2025

Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made and the underlying data generating function is unknown

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

Valid Conformal Prediction for Dynamic GNNs

Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy

ICLR 2025poster

Dynamic graphs provide a flexible data abstraction for modelling many sorts of real-world systems, such as transport, trade, and social networks. Graph neural networks (GNNs) are powerful tools allowing for different kinds of prediction and inference on these systems, but getting a handle on uncerta…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗