Description:
In this course, Students will learn about:
(1) the definition and fundamental models of Social Network Analysis;
(2) network types, structures, models, and dynamic processes on social networks;
(3) calculation methods of the centrality of the social network;
(4) methods for identifying communities in social networks;
(5) software for implementing social network analysis;
(6) visualization of social networks.
This course uses Twitter social network case studies.
Learning Outcomes:
- PLO-1: Able to apply the latest methods and techniques to design and carry out experiments in the field of computing, both in the laboratory and in the field, and analyze the results.
- PLO-2: Able to identify problems and formulate computational solutions to problems in informatics and computers.
Course Learning Outcomes (CLO):
- CLO-1: Explain and apply the network centrality calculation method in Applied Social Network Analysis, as well as analyze and visualize using Social Network Analysis tools.
- CLO-2: Understand and apply the concept of Social Network Analysis in identifying communities in social networks as well as analyzing and visualizing them in graphs.
Lecture Materials:
References:
- Social Network Analysis: Methods and Applications, Stanley Wasserman and Katherine Faust. Cambridge
- Social Network Analysis for Start-Up, Maksim Tsvetovat and Alexander Kouznetsov. O’Reilly. 201
Softwares:
- Gephi
- NetworkX
- Python
