Programmes

Social Network Analysis

  • Summer schools
  • Department of Methodology
  • Application code SS-ME202
  • Starting 2020
  • Short course: Closed
  • Location: Houghton Street, London

UPDATE: Due to the global COVID-19 pandemic we will no longer be offering this course in summer 2020. Please check our latest news on this situation here.

Networks and network data describe an increasingly vast part of the modern world, through connections on social media, communications, financial transactions, and other ties. This course covers the fundamentals of network structures, network data structures, and the analysis and presentation of network data. Students will work directly with network data, and structure and analyse these data using R.

Social networks have always been at the centre of human interaction, but especially with the explosive growth of the internet, network analysis has become increasingly central to all branches of the social sciences. How do people influence each other, bargain with each other, exchange information (or germs), or interact online? A diverse array of deep questions about human behaviour can only be answered by examining the social networks encompassing and shifting around us. Network analysis has emerged as a cross-disciplinary science in its own right, and has in fact proven to be of even greater generality and broader applicability than just the social, extending to ecology, physics, genetics, computer science, and other domains.

This course will examine the key papers in the development of social network analysis, and will develop the theory and methodological tools needed to model and predict social networks and use them in social sciences as diverse as sociology, political science, economics, health, psychology, history, or business. The core of the course will comprise the essential tools of network analysis, from centrality, homophily, and community detection, to random graphs, network formation, and information flow. Alongside this we will read a series of substantive and seminal papers, shaped in part by the interests of the students and their various backgrounds, with a particular focus on the difficult task of causal inference in social networks. The course will also provide an introduction to network modelling, analysis and visualisation using R.

In the classes we will apply the techniques introduced in the associated lectures, using real network datasets, analysed using R. Each class will provide a guided introduction to the techniques, which will then be continued independently in (formative) problem sets, for which model answers will be provided.

 


Session: One
Dates: 22 June  – 10 July 2020
Lecturers:  Dr Eleanor A Power and Dr Milena Tsvetkova

 

Programme details

Key facts

Level: 200 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*: A mid-session take-home exam (worth 30% of the overall grade) and a final take-home exam (worth 70% of the overall grade).

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)


*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment

Prerequisites

An introductory course in probability or statistics.

Programme structure

Specific topics include:

  • Network terminology
  • Egocentric and sociocentric networks
  • Gathering network data
  • Network centrality
  • Structural equivalence, roles, and positions
  • Mutuality, transitivity, and balance
  • Assortativity and community detection
  • Small world networks
  • Power law degree distributions
  • Exponential random graph models
  • Stochastic actor-oriented models

Course outcomes

After successfully completing the course, students will be able to:

  • Construct and describe empirical network data.
  • Identify the main analytical reasons for tailored network analysis tools.
  • Recognize, implement, and interpret the main analytical tools for describing and modeling social network data.
  • Critically approach novel network datasets and analyses.

Teaching

LSE’s Department of Methodology is an internationally recognised centre of excellence in research and teaching in the area of social science research methodology. The Department coordinates and provides a focus for methodological activities at the School, in particular in the areas of graduate student (and staff) training and of methodological research.

Through its graduate programmes, and the Department's provision of courses for research students from all parts of the School, the aim is to make the School the pre-eminent centre for methodological training in the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s methodology faculty.

Reading materials

Core texts:

  • Newman, M.E.J. (2010). Networks: An introduction. Oxford, UK: Oxford University Press.
  • Scott, J. (2017). Social Network Analysis. Los Angeles: SAGE. 4th edition. 
  • Easley, D., and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a highly connected world. New York: Cambridge University Press.

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How to Apply

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