18 citations · 27 across the 9 of their papers we have counts for
13 papers
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…
Scalable Durational Event Models: Application to Physical and Digital Interactions
Cornelius Fritz, Riccardo Rastelli, Michael Fop +1
Durable interactions are increasingly observed in social network analysis with precise timestamps. Phone and video calls, for instance, are events to which a specific duration can…
Statistical Network Analysis with Bergm
Alberto Caimo, Lampros Bouranis, Robert Krause +1
Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerge…
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…
Bergm: Bayesian exponential random graph models in R
Alberto Caimo, Nial Friel
The Bergm package provides a comprehensive framework for Bayesian inference using Markov chain Monte Carlo (MCMC) algorithms. It can also supply graphical Bayesian goodness-of-fit…
Bayesian computational algorithms for social network analysis
Alberto Caimo, Isabella Gollini
In this chapter we review some of the most recent computational advances in the rapidly expanding field of statistical social network analysis using the R open-source software. In…
Bayesian Exponential Random Graph Models with Nodal Random Effects
Stephanie Thiemichen, Nial Friel, Alberto Caimo +1
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Baye…
Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks
Alberto Caimo, Antonietta Mira
Powerful ideas recently appeared in the literature are adjusted and combined to design improved samplers for Bayesian exponential random graph models. Different forms of adaptive M…
Bergm: Bayesian Exponential Random Graphs in R
Alberto Caimo, Nial Friel
In this paper we describe the main featuress of the Bergm package for the open-source R software which provides a comprehensive framework for Bayesian analysis for exponential rand…
Bayesian model selection for exponential random graph models
Alberto Caimo, Nial Friel
Exponential random graph models are a class of widely used exponential family models for social networks. The topological structure of an observed network is modelled by the relati…
Bayesian inference for exponential random graph models
Alberto Caimo, Nial Friel
Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is avail…