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Introduction to Network Analysis

Introduction to Network Analysis

This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.


This course, designed for managers in organizations that have or plan to have their own social networks, teaches a mix of quantitative and qualitative methods to describe, measure and analyze a social network environment. Students learn how to identify influential individuals, track the spread of information through networks, and how to use these techniques on real problems.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Students who complete this course will learn how to:

  • Visualize networks of connections among entities or people
  • Measure attributes of networks
  • Measure attributes of users and ties among them
  • Sample from networks that would be too large for analysis taken as a whole
  • Generate and study hypotheses about networks
  • Analyze propagation of things through networks

Who Should Take This Course

Marketing and IT managers, people who work in organizations with social media presences that they want to manage and analyze, and also people in organizations that have, or plan to have, their own social networks, who want to better understand the details of the environment they create.

Our Instructors

Dr. Jennifer Golbeck

Dr. Jennifer Golbeck

Dr. Jennifer Golbeck is an Associate Professor in the College of Information Studies at the University of Maryland, College Park, and the former director of its Human-Computer Interaction Lab.

Her research focuses on analyzing and computing with social media. This includes building models of social relationships, particularly trust, as well as user preferences and attributes, and using the results to design and build systems that improve the way people interact with information online. She is a Research Fellow of the Web Science Research Initiative and in 2006, she was selected as one of IEEE Intelligent Systems’ Top Ten to Watch, a list of their top young AI researchers.

Course Syllabus

Week 1

Network Analysis Basics

  • Basic Terminology
  • Metrics
  • Visualization

Week 2

The Social Network

  • Tie strength
  • Trust – User attributes and behavior

Week 3


  • Modeling
  • Sampling
  • Content Analysis
  • Propagation

Week 4


  • Location
  • Filtering and recommender systems
  • Business use

Class Dates


03/08/2024 to 04/05/2024
Instructors: Dr. Jennifer Golbeck
09/13/2024 to 10/11/2024
Instructors: Dr. Jennifer Golbeck


03/14/2025 to 04/11/2025
Instructors: Dr. Jennifer Golbeck
09/12/2025 to 10/10/2025
Instructors: Dr. Jennifer Golbeck


There are no prerequisites and no particular background is required. Computer scientists or those with humanities backgrounds will be equally capable of doing the work.

Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

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Register For This Course

Introduction to Network Analysis

Additional Information


In addition to assigned readings, this course also has supplemental video lectures and supplemental readings available online.

Course Text

The required text for this course is Analyzing the Social Web by Jennifer Golbeck.


The required software is Gephi ( Windows users may also want to get NodeXL ( Both are free.

Options for Credit and Recognition

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 3 semester hours in computer science or network analysis. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Introduction to Network Analysis