Social Network Analysis

Monday, June 11, 2018 - Friday, June 15, 2018 8:30 AM - 12:30 PM
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Course Description

This course lays the groundwork of Social Network Analysis (SNA) from a conceptual, mathematical and computational perspective. SNA differs from other analytic perspectives in requirements for data collection, storage, descriptive, and statistical analysis. The course will address each of these by sampling from a range of the most commonly used classes of analytic concepts, demonstrating for each their implementation in primary data collection efforts and empirical analyses (in R).


We will address these concepts around two organizing principles: (1) the two primary theoretical frameworks capturing reasons networks “matter”; and (2) how each class of measures can be applied across different units of analysis: individuals, groups and “whole” networks. While by no means exhaustive, this course will provide students with the beginning toolkit for SNA. SNA is a rapidly advancing field, and these tools are intended to provide the orienting frameworks that can guide further study of SNA on your own.


This course is eligible for an EPIC scholarship. Visit the scholarship application page for more information.

Course Objectives

By the end of the course, participants will be able to:

  1. Understand the primary theoretical & analytic frameworks that underpin SNA.
  2. Grasp the primary strategies for gathering & storing social network data.
  3. Compute & interpret several primary classes of measures, for varying analytic levels.
  4. Describe some of the most commonly observed patterns in empirical networks
  5. Run descriptive & statistical analyses (in R) to identify these patterns in real data.


No formal statistical training or prior experience with R is assumed. However, students' prior familiarity with statistical and computing principles will enhance the course experience, easing the extension of coursework to your own research. Each course module's presentation will conceptually build only from prior material covered in this course. Code templates will be provided for the measurement and computation of each of the introduced concepts. All slides, scripts and data will be posted to dropbox. Participants should bring a computer for personal use (Windows, Mac or Linux), with R previously installed. We will use a number of R packages, which will require that you have privileges on your machine that allow you to install programs/applications. It is also highly recommend that you download and install RStudio. If this is not possible, please contact me in advance for a complete list of the packages you should be sure to have pre-installed.

Course Reading List

Strongly Recommended:

  • O'Malley JA & Marsden PV. The Analysis of Social Networks. Health Services Outcomes Research Methodology 2008;8(4): 222–269.



  • Marsden, PV. 2011. 'Survey Methods for Network Data.' Pp 370-388 in J Scott & PJ Carrington (eds.) The Sage Handbook of Social Network Analysis; Sage.
  • Olgnyanova, Katherine. 2015. “Network Visualization with R.” POLNET Workshop. Available here:
  • Snijders TAB. Statistical Models for Social Networks. Annual Review of Sociology 2011;37:129-151.
  • Journal of Statistical Software Special Issue on STATNET:
  • STATNET Wiki:
  • NOTE: These readings are available in the course's shared drive space


As needed:


Zack W. Almquist, PhD

Zack Almquist is an Assistant Professor of Sociology and Statistics at the University of Minnesota. His research focuses on understanding, modeling, and predicting the effects that space (geography) and time have on human interaction (e.g., sexual contact) and resulting social processes (e.g., information passing or disease transmission). His current projects focus on: (i) measuring and modeling the social support of the homeless, (ii) measuring and modeling the effects of online social interaction on running and biking via the app-based tracking platform STRAVA, and (iii) modeling STD transmission via sexual contact networks in Africa and Southeast Asia. Zack currently serves on the editorial boards of the journals Social Networks and Sociological Methodology, and his research has been published in a number of highly regarded peer-reviewed journals, including: The Journal of Abnormal Psychology, Demographic Research, Geographical Analysis, PLoS One, Political Analysis, Social Networks, Sociological Methodology, Sociological Methods & Research and Statistica Sinica.

Course Fee

Registration for this course is $900.00



The registration period has closed for this event.


Monday, Tuesday, Thursday, Friday: Hammer LL210; Wednesday, Hammer LL110

Hammer Health Sciences Building
701 West 168th Street
New York, NY 10032

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