Module code: SOCM058

Module Overview

In 1967 Stanley Milgram asked 160 people in Omaha, Nebraska, to move a message to a stock broker in Cambridge, Massachusetts, using only a chain of friends and acquaintances. 27.5% of chains were completed using, on average, five intermediate acquaintances (i.e. on average, there were only six degrees of separation between the people in Omaha and the stock broker in Cambridge). Milgram’s study demonstrates that, in some sense, ‘we are all bound together in a tightly knit social fabric.’ Social network analysis helps us understand individuals’ contact with the larger social world. It focuses on relationships between social entities, and analyses patterns of social interaction and their influence on individual behaviour.

This module introduces students to various concepts, methods, and applications of social network analysis drawn from the social sciences. We will start with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. We will then consider descriptive network- and individual-level statistics and their applications in social science research. Finally, we will discuss methods for testing hypotheses about social network structure, and introduce models for social networks. The emphasis will be on applying social network analysis theories and methods to real-world data, and on understanding and interpreting results, rather than on the underlying mathematics.

Module provider


Module Leader

BERLUSCONI Giulia (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 20

Independent Learning Hours: 126

Practical/Performance Hours: 4

Module Availability

Semester 2

Prerequisites / Co-requisites

None. Familiarity with basic mathematical notation and standard statistical methods is an advantage but not essential. Familiarity with R is also an advantage.

Module content

Indicative content includes:  

  • Historical and theoretical foundations

  • Data sources and data collection strategies

  • Graphs, matrices, and sociograms

  • Centrality and centralisation

  • Balance, reciprocity, and transitivity

  • Density and cohesive subgroups

  • Equivalence and blockmodels

  • Dyads and triads

  • Statistical models for social networks

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Individual project report (3,000 words) 80
Oral exam or presentation Individual project oral presentation 20

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate an understanding of social network analysis theories and methods, investigating a chosen topic using network data. Using their own dataset or publicly available ones, students will use social network analysis statistics and models to describe the network and test hypotheses. They will also be required to interpret the results, and discuss their implications.

Thus, the summative assessment for this module consists of:

  • An individual project oral presentation where students will present the preliminary results of their analysis

  • An individual project report (3,000 words) where students will present and critically discuss their results

Formative assessment

On days 1-3 students will complete workshop exercises, and they will discuss the results with each other and the lecturer, who will provide group and individual feedback. Feedback For the summative assessment, students will receive individual, written feedback. The formative assessment includes verbal feedback in class, when students will be asked to work on class exercises which apply their knowledge of social network analysis methods.

Module aims

  • • Provide students with an introduction to theories and methods in social network analysis
  • • Give a clear understanding of data collection and data management strategies
  • • Outline descriptive and inferential methods for social networks commonly used in social research
  • • Enable students to critically evaluate empirical social network research
  • • Provide training to use software to investigate social networks

Learning outcomes

Attributes Developed
001 Describe social network analysis concepts, data collection strategies, and analytic techniques
002 Have a critical understanding of the key network data collection strategies and their potential limitations
003 Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results
004 Be able to implement a social network analysis on real world data and critically evaluate the results
005 Engage with different applications of social network analysis in the social sciences
006 Use social network analysis software to analyse network data

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Expose students to core theories and methods in social network analysis whilst also offering an opportunity to familiarise themselves with the technical and practical issues of analysing network data. Alongside discussion of social network analysis theories and current applications in social research, the use of existing datasets and software during the workshops will give students practical experience of applying theory to novel scientific problems, conducting network analysis, and critically interpreting the results.

The learning and teaching methods include:

  • Lectures

  • Practical workshops

  • Group discussion and feedback

This module is taught intensively over one week. Days 1-3 will include a mix of lectures and workshops. On day 4 students will engage in independent study and preparation for the individual oral presentation. On day 5 students will return to the classroom to present their project and receive feedback.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: SOCM058

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Social Research Methods MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2019/0 academic year.