Social Network Analysis
1. List examples of applications of social network analysis in different areas
2. Explain the main elements (node and link) that make up a network, and how can to characterize networks (degree distributions, clustering coefficient, diameter, average path length)
3. Explain the popular network models and their implications to social sciences
4. Understand the role of social networks in the diffusion and cascading of information
5. Identify the most suitable network metric to identify influencers in a social system (centrality, pagerank)
6. Understand the difference between Strong and Weak ties and their importance in social systems
7. Use network theory in order to design more effective marketing strategies
8. Communicate and discuss the results of a social network analysis
Docente a designar
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
- Barabási, Albert-László. Network science. Cambridge University Press, 2016.
- Easley and Kleinberg, Networks, Crowds, and Markets: Reasoning about a highly connected world. Cambridge Univ. Press, 2010.
- Newman, Networks: An introduction. Oxford Univ. Press, 2010.
- Jackson, Social and Economic Networks. Princeton Univ. Press, 2008.
- Selected scientific manuscripts that will be shared by the teaching staff.
The curricular unit is based on a mix between theo retical and practical lessons with a strong active learning component.
During each session, students are exposed to new concepts and methodologies, case studies and the resolution of examples. Active learning activities (debates, quizzes, mud cards, compare and contrast) will place students at the center of the classroom, so that each takes an active role in the discussion and in the learning process. Computer activities will be done whenever appropriate. For instance, students will build and analyze the class friendship network and thus get some hands on experience and familiarity with the topics of the course.
1. Participation in classroom activities (35%)
2. Presentation and discussion of a selected paper (25%)
3. Final Project with oral presentation and written report ( 40%)
The curricular unit is organized in three Learning Units (LU):
LU1. Introduction to Network Science
LU2. The role of network science in the diffusion of information and behavioral adoption
LU3. Application of network science to marketing
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- Specialization in Knowledge Management and Business Intelligence
- Specialization in Information Systems and Technologies Management
- Specialization in Marketing Intelligence
- Specialization in Marketing Research and CRM
- Specialization in Knowledge Management and Business Intelligence – Working Hours Format
- Specialization in Information Systems and Technologies Management - Working Hours Format
- Specialization in Marketing Intelligence - Working Hours Format
- Post-Graduation in Information Analysis and Management
- Post-Graduation Risk Analysis and Management
- PostGraduate in Data Science for Marketing
- PostGraduate Digital Marketing and Analytics
- Post-Graduation in Knowledge Management and Business Intelligence
- Post-Graduation Information Systems and Technologies Management
- Post-Graduation in Marketing Intelligence
- Post-Graduation Marketing Research e CRM