Exponential random graph (p*) models as a method for social network analysis in communication research

Michelle Shumate*, Edward T. Palazzolo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


Since the 1970s, communication researchers have utilized social network analysis to understand mass, health, organizational, and interpersonal communication. This article introduces communication researchers to a new class of social network analysis methods, exponential random graph (p*) models. This new method represents the latest advancement in social network methodology and will enhance the trajectory of social network research in the communication discipline. The benefits of this class of models include allowing for the simultaneous estimation of attribute and structural parameters, accounting for the interdependent nature of network data, and retaining the complexity of network observations throughout the analysis. An example analysis using data from Shumate, Fulk, and Monge (2005) is provided to illustrate the potentials of exponential random graph modeling. Five different social network software programs capable of the analysis discussed in this article are introduced with regard to their respective benefits. Finally, a brief tutorial based on data from Palazzolo (2005) is given on how to conduct an ERGM analysis using the PNET software program.

Original languageEnglish (US)
Pages (from-to)341-371
Number of pages31
JournalCommunication Methods and Measures
Issue number4
StatePublished - 2010

ASJC Scopus subject areas

  • Communication


Dive into the research topics of 'Exponential random graph (p*) models as a method for social network analysis in communication research'. Together they form a unique fingerprint.

Cite this