Network models are widely used to represent relational information among interacting units.
In studies of social networks, recent emphasis has been placed on random graph models …

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 …

Network models are widely used to represent relational information among interacting units
and the structural implications of these relations. Recently, social network studies have …

This paper presents recent advances in the statistical modeling of random graphs that have
an impact on the representation of social networks. We also consider issues related to the …

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 …

This paper presents recent advances in the statistical modeling of random graphs that have
an impact on the empirical study of social networks. Statistical exponential family models …

The development of stochastic models for the analysis of social networks is an important
growth area in contemporary statistics. The last few decades have witnessed the rapid …

Models and Methods in Social Network Analysis, first published in 2005, presents the most
important developments in quantitative models and methods for analyzing social network …

Statistical models for social networks as dependent variables must represent the typical
network dependencies between tie variables such as reciprocity, homophily, transitivity, etc …

In this chapter, we discuss the importance of the concept of dependence in social network
data. Dependence is usually treated as a technical statistical issue, but in the case of social …