04-800-AC   Social Network Analysis

Course description

There has never been a greater need for data scientists who can accurately analyze data, draw important conclusions, and effectively communicate those conclusions. This course aims to provide insights into the structural analysis of social and information networks, particularly the World Wide Web. Students should gain basic knowledge about network analysis methods and tools as well as their theoretical foundations. Students have the chance to investigate an intriguing online social network topic of their choice within the framework of a real-world data set through the Social Network Analysis class project. Students will have the option to delve further into a chosen subject and develop analytical abilities through the Final Project.

The course will give students the necessary tools and techniques to analyze visual and statistical data, build models, and present findings to support data-driven decisions. It will also give students the skills required for positions in academia and research institutes, as well as roles involving network analysis in entrepreneurship and commercial research.

Learning objectives

Starting from basic graph concepts, we will investigate basic techniques for social network analysis. We will provide the student with a rich and comprehensive catalog of social network analysis tools that can be exploited in the analysis and implementation of a specific website such as social media, eCommerce platforms, and health applications for groups and communities. Working with actual data of their choice allows students to investigate issues that are particularly interesting to them.

Outcomes

Upon completing this course, students will be able to:

• Describe the concepts of Social Network Models and explain the challenges involved.
• Perform a social network analysis task on groups and communities (e.g., how are opinions formed? How is influence propagated?).
• Acquire hands-on experience implementing existing methods and evaluating them over real datasets such as social media website datasets.

Content details

Module 1: Introduction and basic concepts

• Contextualization
• Terminology
• Network data

Module 2: Characterization of large-scale social networks

• Metrics/Centrality Indices
• Origins of network analysis
• Clustering
• Community structure

Module 3: Social Network Analysis and Interpretation

• Visual analysis
• Metrics
• Small World, processes, and evolution

Module 4: Modeling complexity social networks

• A typology of network visualization
• Examples, Historical sources, and networks
• Weighted Social Network
• Temporality/Link Expiration
• Multiplex structure and overlapping communities

Module 5: Homophily and structural changes

• SNA Class Project

Prerequisites

None

Faculty

Carine Pierrette Mukamakuza