This article provides an introductory summary to the formulation and application of
exponential random graph models for social networks. The possible ties among nodes of a …

This book is principally intended for social scientists wanting to undertake social network
research. It assumes the reader already knows a thing or two (perhaps even a lot) about …

Many datasets of interest today are best described as a linked collection of interrelated
objects. These may represent homogeneous networks, in which there is a single-object type …

a posteriori blockmodeling for graphs is proposed. The model assumes that the vertices of
the graph are partitioned into two unknown blocks and that the probability of an edge …

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 …

We describe a methodology to examine bipartite relational data structures as exemplified in
networks of corporate interlocking. These structures can be represented as bipartite graphs …

A major criticism of the statistical models for analyzing social networks developed by
Holland, Leinhardt, and others [Holland, PW, Leinhardt, S., 1977. Notes on the statistical …

The research described here builds on our previous work by generalizing the univariate
models described there to models for multivariate relations. This family, labelled p …

We present network models for social selection processes, based on the p∗ class of models.
Social selection occurs when individuals form social relationships on the basis of certain …

Using simulation, we contrast global network structures—in particular, small world properties—
with the local patterning that generates the network. We show how to simulate Markov graph …