1 code implementation • 29 May 2024 • Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy
Using this, we extend the validity of conformal prediction to dynamic GNNs in both transductive and semi-inductive regimes.
1 code implementation • 14 Nov 2023 • Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy
We propose that a wide class of established static network embedding methods can be used to produce interpretable and powerful dynamic network embeddings when they are applied to the dilated unfolded adjacency matrix.
no code implementations • NeurIPS 2023 • Alexander Modell, Ian Gallagher, Emma Ceccherini, Nick Whiteley, Patrick Rubin-Delanchy
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data.
1 code implementation • 8 Feb 2022 • Alexander Modell, Ian Gallagher, Joshua Cape, Patrick Rubin-Delanchy
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences.
1 code implementation • NeurIPS 2021 • Ian Gallagher, Andrew Jones, Patrick Rubin-Delanchy
We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph.
no code implementations • 12 Oct 2019 • Ian Gallagher, Andrew Jones, Anna Bertiger, Carey Priebe, Patrick Rubin-Delanchy
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.