2015/06/25 by Riccardo Rastelli, Nial Friel, Rastelli, Riccardo +3 · 2 citations
Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Mental Health Research Topics #Methodology (stat.ME) #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.1506.07806
openalex publication_date 2015/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We derive properties of Latent Variable Models for networks, a broad class of models that includes the widely-used Latent Position Models. These include the average degree distribution, clustering coefficient, average path length and degree correlations. We introduce the Gaussian Latent Position Model, and derive analytic expressions and asymptotic approximations for its network properties. We pay particular attention to one special case, the Gaussian Latent Position Models with Random Effects, and show that it can represent the heavy-tailed degree distributions, positive asymptotic clustering coefficients and small-world behaviours that are often observed in social networks. Several real and simulated examples illustrate the ability of the models to capture important features of observed networks.