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07/12/2023
COVID-19 incidence in the Republic of Ireland: A case study for network-based time series models
Network-based Time Series models have experienced a surge in popularity ...
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06/29/2023
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
Over recent years, denoising diffusion generative models have come to be...
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05/25/2023
The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights
In economic and financial applications, there is often the need for anal...
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10/11/2022
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a p...
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09/19/2022
Normal approximation for the posterior in exponential families
In this paper we obtain quantitative Bernstein-von Mises type bounds on ...
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09/01/2022
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
Signed and directed networks are ubiquitous in real-world applications. ...
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05/31/2022
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Synthetic data generation has become a key ingredient for training machi...
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03/28/2022
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
In this work, we introduce DAMNETS, a deep generative model for Markovia...
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03/07/2022
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
We propose and analyse a novel statistical procedure, coined AgraSSt, to...
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02/22/2022
PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs
Signed networks are ubiquitous in many real-world applications (e.g., so...
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02/01/2022
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Recovering global rankings from pairwise comparisons is an important pro...
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01/20/2022
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
In multivariate time series systems, it has been observed that certain g...
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11/01/2021
Bounds for the chi-square approximation of Friedman's statistic by Stein's method
Friedman's chi-square test is a non-parametric statistical test for r≥2 ...
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10/13/2021
SSSNET: Semi-Supervised Signed Network Clustering
Node embeddings are a powerful tool in the analysis of networks; yet, th...
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06/09/2021
DIGRAC: Digraph Clustering with Flow Imbalance
Node clustering is a powerful tool in the analysis of networks. Here, we...
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05/07/2021
Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's method is a collection of tools for analysing distributional com...
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02/28/2021
A Stein Goodness of fit Test for Exponential Random Graph Models
We propose and analyse a novel nonparametric goodness of fit testing pro...
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11/16/2020
Estimating the correlation in network disturbance models
The Network Disturbance Model of Doreian (1989) expresses the dependency...
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04/03/2020
Relaxing the Gaussian assumption in Shrinkage and SURE in high dimension
Shrinkage estimation is a fundamental tool of modern statistics, pioneer...
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12/02/2019
Core-Periphery Structure in Directed Networks
While studies of meso-scale structures in networks often focus on commun...
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06/19/2019
On infinite covariance expansions
In this paper we provide a probabilistic representation of Lagrange's id...
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06/19/2019
First order covariance inequalities via Stein's method
We propose probabilistic representations for inverse Stein operators (i....
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01/02/2019
Anomaly Detection in Networks with Application to Financial Transaction Networks
This paper is motivated by the task of detecting anomalies in networks o...
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06/10/2018
Bounds for the asymptotic distribution of the likelihood ratio
In this paper we give an explicit bound on the distance to chisquare for...
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04/02/2017