4 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…
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 ubiquitous in social network analysis and are increasingly observed with precise time stamps. Phone and video calls, for example, are events to which a spe…
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…