Social network analysis has achieved remarkable popularity in disease ecology, and is
sometimes carried out without investigating spatial heterogeneity. Many investigations into …

The analysis of network data is an area that is rapidly growing, both within and outside of the
discipline of statistics. This review provides a concise summary of methods and models used …

We estimated ideological preferences of 3.8 million Twitter users and, using a data set of
nearly 150 million tweets concerning 12 political and nonpolitical issues, explored whether …

Network models are widely used to represent relations between interacting units or actors.
Network data often exhibit transitivity, meaning that two actors that have ties to a third actor …

Social network data often involve transitivity, homophily on observed attributes, community
structure, and heterogeneity of actor degrees. We propose a latent cluster random effects …

This unique textbook provides an introduction to statistical inference with network data. The
authors present a self-contained derivation and mathematical formulation of methods …

Abstract latentnet is a package to fit and evaluate statistical latent position and cluster
models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to …

We review the broad range of recent statistical work in social network models, with emphasis
on computational aspects of these methods. Particular focus is applied to exponential-family …

In a short introduction on social network analysis, the main characteristics of social network
data as well as the main goals of social network analysis are described. An overview of …

The latent space item response model (LSIRM) is a newly-developed approach to analyzing
and visualizing conditional dependencies in item response data, manifested as the …