7 citations · 12 across the 10 of their papers we have counts for
10 papers
The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers
Minh Triet Pham, Ian Gallagher
Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different results when applied to the sam…
Generator-based Graph Generation via Heat Diffusion
Anthony Stephenson, Ian Gallagher, Christopher Nemeth
Graph generative modelling has become an essential task due to the wide range of applications in chemistry, biology, social networks, and knowledge representation. In this work, we…
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees
Emma Ceccherini, Ian Gallagher, Andrew Jones +1
Stability for dynamic network embeddings ensures that nodes behaving the same at different times receive the same embedding, allowing comparison of nodes in the network across time…
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…
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…
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…
Spectral embedding and the latent geometry of multipartite networks
Alexander Modell, Ian Gallagher, Joshua Cape +1
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of a properly constructed matrix, and has found applications throughout science…
Spectral embedding for dynamic networks with stability guarantees
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 indi…
Persistent Homology of Graph Embeddings
Vinesh Solanki, Patrick Rubin-Delanchy, Ian Gallagher
Popular network models such as the mixed membership and standard stochastic block model are known to exhibit distinct geometric structure when embedded into using…
Spectral embedding of weighted graphs
Ian Gallagher, Andrew Jones, Anna Bertiger +2
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asympt…