To analyze social network data using standard statistical approaches is to risk incorrect
inference. The dependencies among observations implied in a network conceptualization …

The last decade has seen substantial advances in statistical techniques for the analysis of
network data, as well as a major increase in the frequency with which these tools are used …

Methods for descriptive network analysis have reached statistical maturity and general
acceptance across the social sciences in recent years. However, methods for statistical …

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 …

Baseline models have been used in the analysis of social networks as a way to understand
how empirical networks differ from “random” ones. For the purposes of social network …

The exponential random graph model (ERGM) is an increasingly popular method for the
statistical analysis of networks that can be used to flexibly analyze the processes by which …

This article investigates the role of resource dependence in explaining the social structure of
policy networks while controlling for the effects of microstructure, such as the tendency for …

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

Written by a sociologist, a graph theorist, and a statistician, this title provides social network
analysts and students with a solid statistical foundation from which to analyze network data …

This article provides a tutorial review of some fundamental ideas and important methods for
the modeling of empirical social network data. It describes basic concepts from graph theory …