NUSC School in Network Analysis 2022

Summer School 2022

About us

The University of Greenwich Networks and Urban Systems Centre has multi-disciplinary expertise exploring the expanding frontiers of urban challenges and opportunities to improve quality of life, competitiveness and sustainability. With expertise in transport, supply chain and social network systems, we focus primarily on:

  • Distributed and overlapping production systems (e.g. IT clusters, health);
  • Healthy and high-quality urban ecosystems;
  • Business models for technologically transformed social systems;
  • Global value chains

We have the largest concentration of business network analysts in Europe, applying the techniques of organisational network analysis to a wide range of business problems, re-conceiving individual firms, organisations and markets as structured relationships.

Our experts have published widely and are working on a range of current research projects including knowledge transfer within the creative industries, high-tech industrial clusters, diffusion through networks, enhanced networking with social media, black and minority ethnic career support networks and inter-organisational networks in global value chains.

NUSC Summer School in Network and Data Science

Mon 20th - Sat 25th June 2022

The NUSC Summer School provides opportunities for those both new to network and data science and those who wish to consolidate or expand existing knowledge in the field. Three distinct courses offer an introduction to social network analysis, a workshop on social media and text-mining with R, and an introduction to relational event modelling. The courses will be  provided in an in-person, campus environment, in the iconic UNESCO world heritage site of the University of Greenwich, in London.

The courses are aimed to equip postgraduate students, researchers and social science practitioners with skills to apply in practical projects. This is an in-person event only.


Each course runs 10:00-16:00 each day:

  1. Doing Research with SNA: Tools, Theories, and Applications June 20th-22nd.
  2. Social Media and Text Mining in R, June 23rd.
  3. Relational Event Models (REMs) for the Analysis of Social Networks: A Hands-on Introduction, June 24th-25th.

Course Descriptions

1. Doing Research with SNA: Tools, Theories, and Applications

Instructor: Bruce Cronin

The goal of the course is to provide attendees with a general overview of the field of social network analysis, confidence in using its key analytical tools in practice, and insight into how it can be used in scholarly practice in the social, economic, managerial and political disciplines. The focus is on research design and how SNA elements can be successfully integrated into a research project, paper, or dissertation. Participants will be introduced to UCINET and Netdraw software via practical exercises

All social science backgrounds are welcome, and participants are assumed not to have any previous knowledge of SNA, or of any analytical or statistical software. No previous experience with the software is expected.

At the end of the course participants will be able to:

  1. independently design a research programme requiring SNA in their own field of research
  2. collect and manage network data;
  3. analyse, interpret and visualise fundamental network measures at the individual, group and network level;
  4. confidently use UCINET and NetDraw to perform network analysis and visualise network data.

Bruce Cronin is Professor of Economic Sociology at the University of Greenwich, where he is co-director of the Networks and Urban Systems Centre

General references
Borgatti, SP, Everett, MG and Johnson, JC (2018) Analysing Social Networks, 2nd Edition. London: Sage.

2. Social Media and Text Mining in R

Instructor: Dr Mu Yang

An introduction to social media analytics and text mining with the R-programming language.

Participants should have an elementary knowledge of the R-programming language.

At the end of the course participants will be able to:

  1. Make use of key metrics used for analysing social media,
  2. Undertake sentiment analysis on user-generated-content on social media,
  3. Employ topic modelling for identifying trends in social data

Dr Mu Yang is Senior Lecturer in Digital Marketing Analytics at the University of Kent, where she is Interim Director of TIME Research Centre.

3. Relational event models (REMs) for the analysis of social networks: A hands on Introduction

Instructors: Jürgen Lerner and Alessandro Lomi

Networks of social relations and communication networks frequently generate information on repeated interaction over time. This information typically takes the form of relational event sequences - streams of time-ordered events connecting social actors. Examples of relational events are common. Conversations, email communication, interaction among members of teams, participation in social gatherings or in peer-production projects, are all examples of interactive settings that may generate observable streams of relational events.

The goal of this workshop is to provide participants with an introduction to relational event modeling - both for dyadic events (having one sender and one receiver) and for "hyperevents" connecting any number of participants. The workshop involve hands-on experience with software specifically designed for specifying end estimating relational event models on actual data, including the open-source software eventnet (

The workshop is targeted at participants interested in statistical modeling of networks based on relational event data. Participation to the workshop does not assume any particular prior knowledge or experience with statistical models for social networks. Participants are invited to informally share their own research questions, which may possibly be addressed by a REM analysis, prior to or during the workshop.

By the end of the workshop participants will be able to:

  • Design and apply REMs in their own empirical study;
  • Understand variations of REMs including typed or weighted events, multi-mode networks, actor-level or dyad-level attributes;
  • Implement sampling strategies to fit REM to large event networks;
  • Understand the foundation of relational hyperevent models for analyzing multicast relational events;
  • Read, understand and comment on current research papers based on REMs

Jürgen Lerner is interim professor for Computational Social Sciences and Humanities at the RWTH Aachen. Alessandro Lomi is a professor at the University of Italian Switzerland (Lugano) where he directs the Social Network Analysis Research (SoNAR) Center

General references
Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155-200.
Lerner, J., & Lomi, A. (2022). A dynamic model for the mutual constitution of individuals and events. Journal of Complex Networks, 10(2), cnac004.


Early Bird offer ends on Friday 21 May at 5pm

Doing Research with SNA: Tools, Theories, and Applications:

  • General  £300 (Early Bird £250)
  • Student  £200  (Early Bird £150)

Social Media and Text Mining in R:

  • General  £120  (Early Bird £100)
  • Student  £80 (Early Bird £60)

Introduction to Relational Event Modelling:

  • General  £240  (Early Bird £200)
  • Student  £160 (Early Bird £120)
Registration has now closed

*Staff and Students who are part of the Business Faculty, University of Greenwich are entitled to a discounted rate.

If you are unsure about which ticket you are to purchase, please contact us.

Find Hamilton House

Located in Park Vista, next to Greenwich park, a short walk from the main Greenwich Campus

Upon arrival to Hamilton House, please ring the buzzer on the left-hand side of the door and report to the reception upon entry.

Unfortunately, the Hamilton House building has no disabled access and there is no on-site parking available.

Learn more about travelling to Hamilton House.

  • DLR Cutty Sark (approx. 11 min walk)
  • Maze Hill (approx. 3 min walk)
  • Greenwich (approx. 15 min walk)
  • Greenwich Pier - Ferry service (11 min walk)
  • TFL buses frequently run close by.
  • Public 'Pay and display' car parks nearby.
  • Campus bus service between campuses.