115 citations · 116 across the 8 of their papers we have counts for
6 papers · 1 filter
Bayesian perspectives on exponential random graph models
Alberto Caimo, Isabella Gollini
Exponential random graph models (ERGMs) are a widely used framework for network data, enabling hypothesis testing on the structural mechanisms underlying observed networks. Bayesia…
Multi-layer dissolution exponential-family models for weighted signed networks
Alberto Caimo, Isabella Gollini
Understanding the structure of weighted signed networks is essential for analysing social systems in which relationships vary both in sign and strength. Despite significant advance…
Separable models for dynamic signed networks
Alberto Caimo, Isabella Gollini
Signed networks capture the polarity of relationships between nodes, providing valuable insights into complex systems where both supportive and antagonistic interactions play a cri…
A multilayer exponential random graph modelling approach for weighted networks
Alberto Caimo, Isabella Gollini
A new modelling approach for the analysis of weighted networks with ordinal/polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network conne…
Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Data
Isabella Gollini, Thomas Brendan Murphy
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical d…
Joint Modelling of Multiple Network Views
Isabella Gollini, Thomas Brendan Murphy
Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional…