Overview

Each of us are bound by ties to other individuals; e.g., ties of friendship, affiliation, and responsibility. These are social networks. Network science allows us to understand and make sense of human behavior in terms of such relations, whether at the individual, organizational or semantic level. This course will provide an overview of network science and the measures and tools used in a network analysis.

Sample instructor(s)

Kathleen M. Carley, L. Richard Carley

Duration

6 hours - [This could be 3, 6, 9, 12, 18]

Customizable?

Yes, this course can be tailored towards professionals with more of a technology background, or more of a policy background. The 3-hour version of the course will provide a high-level overview and focus on a subset of course topics, the 6 and 9 hour ones will include an expanded list of topics and demonstrations, while the 12- and 18 hour version will cover all listed topics and include interactive activities for hands-on learning.

In-person or remote

There are three variations: remote, in-person, or a hybrid model with live discussion in a webinar and pre-recorded sessions.

Intended audience

This course is appropriate for professionals working in either technology or policy.

Takeaways

Level 1: What is network science. History of network science. Basic features of networks that constrain and enable every day life. Role of network science in assessing organizations, pandemics, social media, terrorists. Types of tools and data.

Level 2: Level 1 + Basic metrics. Community Detection. Visualization. Multi-modal networks. What-if reasoning with networks. Demonstrations of key software.

Level 3: Level 2 + Experience analyzing networks. Advanced metrics. Advanced visualization. Using network analysis to assess social media. Data management issues.

Course topics

  • Nature of network science
  • History of network science
  • Network constraints and enablers
  • Networks and organizations
  • Networks and social media
  • Networks and terrorism
  • How to analyze and visualize network data.
  • Such data can include:
    • survey data,
    • social media data,
    • netflow data,
    • social media data,
    • cyber-attack data,
    • terrorism data
    • bibliometric data. 
  • Key actor metrics
  • Community detection
  • Network visualization
  • High dimensional and multi-modal networks
  • Data management

Prerequisites

Basic rudimentary statistics such as mean and variance.

Familiarity with excel.

Materials

Key papers, PDF of slides, and in the longer courses, sample data will be provided.

Contact us

To learn about our custom programs and any upcoming open enrollments, reach out to Michael Lisanti.