3 papers
stat.ME2026
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…
stat.ME2026
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…
stat.ME2025
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…