3 papers
stat.CO2024
Valid Bootstraps for Network Embeddings with Applications to Network Visualisation
Emerald Dilworth, Ed Davis, Daniel J. Lawson
Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous r…
stat.ML2024
Valid Conformal Prediction for Dynamic GNNs
Ed Davis, Ian Gallagher, Daniel John Lawson +1
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 p…
cs.SI2023
A Simple and Powerful Framework for Stable Dynamic Network Embedding
Ed Davis, Ian Gallagher, Daniel John Lawson +1
In this paper, we address the problem of dynamic network embedding, that is, representing the nodes of a dynamic network as evolving vectors within a low-dimensional space. While t…