Social Network Analysis

Monday, June 5, 2017 - Friday, June 9, 2017 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.

Course Learning 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. 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.

NOTE: These readings are available in the course's shared drive space

As needed:



jimi adams, PhD

The focus of jimi adams' research is on how networks promote or constrain the spread of things like diseases and ideas through a population. Increasingly, this work focuses on how interdisciplinary scientific fields are arranged and evolve through time. Previously, this has involved examining patterns that contribute to HIV/AIDS transmission and prevention in the US and sub-Saharan Africa. Dr. adams’ previous SNA-related teaching includes a conceptually oriented survey course for undergraduates, a graduate seminar on social network data collection methods in health research, and SNA (both for graduate students and for 4 previous years at EPIC) - each drawing students from a wide-range of disciplinary backgrounds.

Course Fee

Registration is $900.00

DISCOUNT of 10% will be applied at checkout for all registrations on or before April 1st.


The registration period has closed for this event.


Monday: Hammer 303; Tuesday-Friday: Hammer LL110

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

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