30389 - SOCIAL NETWORK ANALYSIS (Introduction to Network Science)
Course taught in English
Go to class group/s: 31
We are increasingly living in a small world, where everything is connected to everything else: from economic markets to the Internet to disease outbreaks to our group of friends. Relationships and flows of information among people and organizations form complex systems that are the fundamental structures governing our world, yet defy easy understanding. To analyze these interconnected systems, we must turn to network science. This course provides an intensive introduction to the field of social network analysis with an emphasis on business settings. The course is divided into traditional lecture sessions and exercise (laboratory) sessions. The overarching goal is to familiarize students with the theory, research, methodological issues, and practical implications connected with the analysis of relational data. Upon completion of the course, students should have a good grasp of social network concepts and methods, and be able to use them. The approach is very practical and it involves concrete uses of social network data during the lab sessions. This means mastering not only software tools, but also statistical and analytical strategies. Students need to bring their own laptop to effectively participate in the lab sessions. This perspective is integrated with a practitioner approach by using examples from consulting engagements.
- Social network theories, concepts, and terminology (e.g., structural holes, social capital, social influence, origins and evolutions of network structures).
- Using matrices and graphs to represent social relationships (e.g., one-mode and two-mode networks, layout algorithms, network visualizations).
- Methods and measures to understand network data (e.g., centrality algorithms, cliques and communities, positions and roles, scale-free networks).
- Applications of social network analysis (e.g., strategic alliances, organizational change, key-player detection).
At the end of the course, students should be able to:
- Explain the most important social network theories and identify their application to practical managerial problems and contexts.
- Recall the main terminology and define concepts associated to the analysis of social networks.
- Illustrate the main social network measures and statistical techniques that can be used to analyze relational data.
- Contrast different ways of visualizing social networks and illustrate the implications of their use.
- Articulate the strengths and limitations of the social network approach.
At the end of the course, students should be able to:
- Apply social networks concepts to aid practical managerial decisions.
- Examine a business situation through a social network perspective to determine management needs.
- Improve their ability to establish and maintain effective social networks.
- Design social network surveys to collect and analyze relational data.
- Employ statistical techniques and social network software to calculate different social network measures.
- Create detailed social network reports to communicate results in an effective way, including compelling and powerful network visualizations.
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Case studies /Incidents (traditional, online)
- Group assignments
- Interactive class activities (role playing, business game, simulation, online forum, instant polls)
The course leverages a blend of methods aimed at complementing each other and optimizing the learning experience.
- Lectures are used to discuss social network theories and concepts as well as the technical aspects associated with the collection and analysis of social network data. During such lectures, students also have the chance to work with case studies, interactive class activities, and short individual exercises that help them understand the peculiarities associated with these type of data.
- Lab sessions provide students with a hands-on experience of the topics and methods discussed in class. These practice sessions focus on issues related to both research design and data analysis, and they require the use of personal computers. The specific software that will be used is UCINET, which includes the standard tools used in social network analysis. All students are required to download on their PC the latest version of UCINET (https://sites.google.com/site/ucinetsoftware/home).
- Finally, students also put their knowledge in practice by participating to a group project. Putting on their “network consulting” hat, students will analyze and present network data in class to the instructor and other students toward the end of the course. This activity will allow them to experience first-hand the challenges associated with analyzing and presenting network data to different stakeholders.
|Continuous assessment||Partial exams||General exam|
Class attendance is strongly encouraged. Attending students are evaluated based on the following three criteria:
- In-class contribution (10% of the final grade) aimed to test the students’ ability to interact in a constructive way and present their points of view in an effective way in both face-to-face lectures and lab sessions. Attendance and punctuality will also be considered.
- Group project (30% of the final grade) aimed to test the students' critical application of the network concepts and methodologies learned during the course. Moreover, the group project allows to test students' ability to present their results in an effective way in both written and oral form.
- Final written exam (60% of the final grade) that includes both open- and close-ended questions, aimed to test students' knowledge of the main theories, terminology, and concepts associated to the study of social networks, as well as the statistical techniques and software used to analyze different types relational data.
Non-attending students are evaluated only on the basis of a final written exam that includes both open- and close-ended questions, aimed to test students' knowledge of the main theories, terminology, and concepts associated to the study of social networks, as well as the statistical techniques and software used to analyze different types relational data.
- Lecture slides and references to articles indicated at the end of the lecture slides.
- Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Available on line free of charge at http://faculty.ucr.edu/~hanneman/nettext/.
- Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks (2nd edition). SAGE Publications Limited.
- In addition to lectures, the course has also some lab-exercise sessions. Problem sets and their solutions will be posted in the Bboard platform of the course. Required software: UCINET (https://sites.google.com/site/ucinetsoftware/home).