41 citations · 88 across the 9 of their papers we have counts for
9 papers
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
Ruikang Ouyang, Andrew Elliott, Stratis Limnios +2
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representati…
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu +3
Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating…
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Jase Clarkson, Mihai Cucuringu, Andrew Elliott +1
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based…
Motif-Based Spectral Clustering of Weighted Directed Networks
William George Underwood, Andrew Elliott, Mihai Cucuringu
Clustering is an essential technique for network analysis, with applications in a diverse range of fields. Although spectral clustering is a popular and effective method, it fails…
Core-Periphery Structure in Directed Networks
Andrew Elliott, Angus Chiu, Marya Bazzi +2
While studies of meso-scale structures in networks often focus on community structure, core--periphery structures can reveal new insights. This structure typically consists of a we…
Anomaly Detection in Networks with Application to Financial Transaction Networks
Andrew Elliott, Mihai Cucuringu, Milton Martinez Luaces +2
This paper is motivated by the task of detecting anomalies in networks of financial transactions, with accounts as nodes and a directed weighted edge between two nodes denoting a m…