61 citations · 294 across the 31 of their papers we have counts for
36 papers
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
Alexander Modell, Ian Gallagher, Emma Ceccherini +2
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples , each representing a time-stampe…
Hierarchical clustering with dot products recovers hidden tree structure
Annie Gray, Alexander Modell, Patrick Rubin-Delanchy +1
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant o…
Implications of sparsity and high triangle density for graph representation learning
Hannah Sansford, Alexander Modell, Nick Whiteley +1
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inne…
Statistical exploration of the Manifold Hypothesis
Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold,…
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
Michael Whitehouse, Nick Whiteley, Lorenzo Rimella
Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the…
Matrix factorisation and the interpretation of geodesic distance
Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions. We show that this can be accompl…