Network Analysis I: Introduction


  • James Wilson, University of San Francisco

This course will lay the groundwork for social network analysis (SNA) from conceptual, statistical, empirical and computational foundations. We will draw on the rich multidisciplinary history that has shaped the field's development - incorporating perspectives from sociology, physics, mathematics, statistics, and public health.

SNA differs from other analytic perspectives in ways that require unique strategies for data collection, storage, descriptive and statistical analyses. The course will address each of these strategies by surveying a range of the most commonly used analytic techniques. We will demonstrate their empirical applications and computation using the R programming language.

We will discuss topics including data collection, network representation, summaries of networks including social balance, distance, density, and centrality, clustering, and an introduction to statistical models for large scale inference. While by no means exhaustive, this introduction will provide an overview of the fundamental tools available for SNA.

Software: All analyses in this course will be conducted in RStudio (using packages like igraph, statnet, gergm, and ergm packages), with basic prior experience with R required.

Fees: Consult the fee structure.

Tags: network analysis, social structures, sociogram, dyadic analysis, graph theory, social network diagram, actor interrelations

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 26 - July 21

Time: 1:00 PM - 3:00 PM


  • James Wilson, University of San Francisco