MSc Social Network Analysis

This course explores the following weekly topics:

1. Introduction to Ucinet and Data management
2. Visualization in Netdraw
3. Centrality and hierarchical clustering
4. Cohesion
5. Egonets and structural holes
6. Cohesive subgroups
7. 2 mode
8. Testing Hypothesis
9. Roles and equivalence
10. XUcinet in R

The unit aims to introduce students to the tools to map and analyse the patterns of relations that link individuals or groups i.e. social networks. Taking this perspective allows us to develop theoretical concepts and methods which enable us to uncover and understand how the patterns in the social relations that bind us together influences our behaviours, attitudes and beliefs.

Each week contains an hour face to face lecture, followed by two hours workshop in a computer lab.

• Critically engage with the theoretical foundations of SNA and use them to formulate robust and coherent SNA empirical questions.
• Understand the variety of network data, ie. egonets, whole networks, multilevel networks, longitudinal networks, multimode networks.
• Assess the feasibility and applicability of a wide range of analytical techniques to social network data
• Critically understand and evaluate SNA research, and reflect upon methodology in a theoretically informed way.
• Understand research questions in multidisciplinary contexts, and efficiently operationalize them.

• Reflect upon and evaluate theoretical ideas, and construct theoretical arguments to support their work.
• Analyse network structures using descriptive measures.
• Write social network analysis scientific articles and reports.

• Manage, and analyse various type of social network data, both qualitative and quantitative.
• Produce cutting edge data visualization with high impact and informativity.
• Be proficient in software that handle network data (Ucinet)

• Develop new or enhanced skills to identify and use diverse social network data, and use such data to inform cutting edge research projects and interventions in a variety of contexts.
• Understand and mediate multidisciplinary environments and liaise across different intellectual and practical contexts
• Work collaboratively, both face-to-face and through the use of various online tools and spaces.
• Accurately and effectively work with numbers, and use advanced computational software.

3000 word computer based exercise 100%