The second of five courses where you learn how to select appropriate data types to represent a dataset in a C++ program.
Module
Social Media and Network Science
Module information>
This module introduces students to the theory and techniques of graph analysis (credit bearing).
By taking this module, you will have an opportunity to master the theory and practice of graph analysis and social media data analysis. You will be shown how to gather datasets from public social networking platforms. You will learn how to gather social network data, how to convert social media data into graph representations and then apply typical algorithms to analyse the structure of the graph. You will visualise graphs and assess data flow and influence between the different nodes in the graphs.
Topics covered
- Network Elements: Introduction and Basic Definitions
- Understanding Social Media History and its key concepts (background, structures, and challenges)
- Networks Paths and Distances
- Mining Social Media Data: Understand Data Structure and Retrieving the data
- Network Hubs: Node Centrality
- Mining Social Media Platforms
- Network Links: Directions and Weights
- Visualising Networks for Social Media Data
- Network Communities
- Generating graph for Social Media Data
Credits
15 (150 hours)
Assessment
- Coursework item 1 (15%)
- Coursework item 2 (15%)
- Coursework item 3 (70%)
