Advances in Exponential Random Graph Models

This chapter presents recent developments in exponential random graph models (ERGMs), statistical models for social network structure. ERGMs assume that social networks are composed of various network substructures (or network configurations) like reciprocity, brokerage, or transitive closure, which, combined together, explain how the network came into being. The chapter also discusses recent developments for related models—auto-logistic actor attributes models (ALAAMs)—that examine social influence effects. The chapter focuses on three new types of models that have developed in the past few years: directed network models for social influence, multilevel extensions of ERGMs, and multilevel extensions of ALAAMs. The chapter concludes with three empirical applications to demonstrate what new possibilities exist in the application of these new statistical models for social networks to social science questions.