ARS'25 Tenth International Workshop on Social Network Analysis

Session

Not scheduled

Naples (Italy)

Contribution list Timetable

157. A Zero-Inflated Poisson Latent Position Cluster Model

Riccardo Rastelli (University College Dublin)

Statistical approaches for clustering and community detection in complex networks

Oral presentation

Statistical approaches for clustering and community detection in complex networks

The latent position model is a popular approach for the statistical analysis of network data. A key aspect of this model is that it assigns nodes to random positions in a latent space, and the probability of an interaction between each pair of nodes is determined by their distance, allowing researchers to visualize nuanced structures via a latent embedding of the graph. Missing data is a...

185. Dynamic clustering of bipartite networks via hidden Markov latent trait analyzers

Prof. Maria Francesca Marino (‪Università degli Studi di Firenze)

Statistical approaches for clustering and community detection in complex networks

Oral presentation

Statistical approaches for clustering and community detection in complex networks

We propose a dynamic extension of the Mixture of Latent Trait Analyzers (MLTA) for a bipartite network. Specifically, we move along a Hidden Markov Model framework to account for the dynamic nature of the data and enable a dynamic clustering of sending nodes over time. A multidimensional continuous latent variable (trait) is assumed to account for residual, unobserved, time-constant, latent...

160. Emergence of scaling and organization in microblogging platforms: the emblematic examples of BlueSky and Truth Social

Mr. Tommaso Bertola (University of Padua)

Statistical approaches for clustering and community detection in complex networks

Oral presentation

Statistical approaches for clustering and community detection in complex networks

We present an analysis of the complex network structure of some microblogging platforms, comparing their internal organizations and, for the case of BlueSky, its growth during a period of massive migration from X/Twitter. Topological differences are the result of platforms’ functionalities and of individuals’ behaviors. At the same time, the sudden increase of users in BlueSky acts as a large...

159. Predicting Links in temporal Networks using a dynamic Stochastic Block Model

Luca Brusa (University of Milano-Bicocca)

Statistical approaches for clustering and community detection in complex networks

Oral presentation

Statistical approaches for clustering and community detection in complex networks

Longitudinal social network data, observed as discrete-time snapshots, can be modeled using the dynamic stochastic block model, which assumes that nodes are clustered into unobservable groups and that block memberships evolve according to a hidden Markov chain. This model can be used as an early warning system by predicting the occurrence of future links through the evolution of latent block...