Social Network Analysis

Key topics covered

Monday

You will start by learning why and how network analysis matters in social research and in practice. We will cover social and political network analysis, organisational network analysis, and social movements. We unpack theories of tie formation, and mechanisms at work in networked phenomena (trust, formal and informal interactions, popularity, brokerage, reciprocity, transitivity, assortativity, preferential attachment, clustering, etc). You will then make the transition from research questions and theory to appropriate data and research designs. 

Tuesday

You will connect network structures and processes with social functions (measures at the network level; null models and rewiring), test hypotheses about how relational mechanisms work, and discuss the empirical implications in different contexts (inequality, competition, influence, etc).

We encourage you to use your own data or make your own data collection strategy during the class. 

Wednesday

We focus on group formation theories and measurement techniques (measures at the community level; clustering, transitivity, community detection and motif discovery algorithms). You'll learn how cohesive groups form in different contexts, and what implications clustering has for community building, belief reinforcement and change, information transmission, etc.

Thursday

You will connect actors’ network positions with importance and roles (measures at the individual level ꟷ centrality measures), and test how different node-level mechanisms work in different contexts (brokerage, popularity, influence, etc) and the implications they have for strategic behaviour (structural holes, structural folds, boundary spanners, influencers, etc).

Friday

We wrap everything up into a discussion on complexity theory (emergent phenomena and unintended consequences), regression models for networks (ERGMs & SAOMs), opportunities and challenges in network inference (social selection vs social influence) and a short showcase of the measurable impact of SNA solutions in organisations.

How the course will work

This course is the perfect opportunity to make the best of student-centred teaching and learning. Its design has everything you need for an enriching and empowering learning experience:

Pre-course materials to prepare you for the live sessions

• Readings
• Guided tours of software use and functionality
• Options for dataset hunting and data collection

Live sessions with dynamic content

• Conceptual discussions
• Class exercises
• Group activities
• One-on-one research problem consultations
• Multimedia content

Independent class participation

• Practical homework assignments
• Fun quizzes
• Final projects on data and topics of choice

The materials we share are designed to create and engage a long-term community of learners, which should give you support to rely on long after this course has ended. 

The technical and project management skills and instruments you will learn and use are an added bonus to your substantive learning experience.

In this class, we will use Gephi and R & RStudio (Posit). Please have all three software installed and working by the time we begin. If you will use a university or work laptop, please check whether new software can be independently installed on your machine or if you need the IT Department to allow some permissions. 

None for this class. We welcome researchers and practitioners with any disciplinary background or work experience.