IEMS 341: Social Networks Analysis



The course has no formal pre-requisites but will be most beneficial to students who have had an introductory statistics course, and who are willing to learn the basics of running code in R (but need not have prior coding experience). You will be asked to run basic (point and click, cut and paste) pre-written code to visualize and analyze networks. The primary goal of the course is not to learn advanced programming for social network analysis (SNA), but to learn to interpret and gain actionable insights from the results of network analysis.


This course is cross listed in both Industrial Engineering and Communication Studies (COMM-ST 352). The material of the course is the same for students in either section.

Digitally technologies are transforming the way we learn, organize, innovate, mobilize, trade, travel, date, and play. This course introduces "network thinking" as a way to examine how our social networks enable and constrain our attitudes and behaviors, personally and professionally, as individuals and collectives. Social network analysis encompasses both the online and offline relationships we leverage in our personal and professional lives.

The course will discuss how social networks concepts, theories, and methods help us map, measure, understand and leverage social networking platforms (e.g., Facebook, Twitter, LinkedIn, Instagram, Snapchat), enterprise social media (e.g., Slack, Microsoft Teams, Facebook Workplace), recommender systems (e.g., Amazon, Netflix, Spotify), financial transaction networks (e.g., Venmo, PayPal, Ripple), sharing economy websites (e.g., Uber, Airbnb, Instacart, Upwork) and dating websites (e.g., Tinder, Grindr, Match, eHarmony).

This course counts as a “Management Science” elective for Industrial Engineering degree requirements.


  1. Understand social networks and the roles that they play in our day-to-day lives.
  2. Compute and interpret metrics that describe individual nodes in a network.
  3. Compute and interpret metrics that characterize various qualities of the network as a whole.
  4. Compute and interpret partitioning networks into communities based on different criteria.
  5. Specify, estimate and interpret statistical models of network dynamics and collective behavior.
  6. Make the results of network analytics actionable for practitioners.


  • Social network concepts and theories
  • Network visualization
  • Descriptive network analysis
  • Statistical network analysis
  • Network data collection
  • The impact of professional networks in organizations


All reading materials will be made freely available on the course Canvas site.

All software for the class (R and RStudio, various packages in R) is freely downloadable.