3 papers
stat.ML2026
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…
stat.ML2026
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…
stat.ML2025
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…