秋葵视频

NUSC Summer School in Network and Data Science

Mon 19th - Fri 23rd June 2023

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. Six distinct courses offer an introduction to social network analysis, organisational network analysis with xUCINET, text mining for literature reviews, a workshop on social media and text-mining with R, an introduction to relational event modelling, and the use of R and GitHub for research. The courses will be  provided in an in-person, campus environment, in the iconic UNESCO world heritage site of the 秋葵视频, 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.

Programme

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

1. Doing Research with SNA: Tools, Theories, and Applications, June 19th-21st.
2. Organisational Network Analysis with xUCINET in R, June 22nd-23rd.
3. Topic Modelling for Literature Reviews, June 19th
4. Social Media and Text Mining in R, June 20th.
5. Relational Event Models (REMs) for the Analysis of Social Networks: A Hands-on Introduction, June 21st-22nd.
6. R and Github for Research, June  23rd.

Course Descriptions

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

Instructors: Srinidhi Vasudevan and Anna Piazza

About:
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

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

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

Requirements
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.

Instructor
Dr Srinidhi Vasudevan is a lecturer in Business Management at the  秋葵视频. Dr Anna Piazza is  a lecturer in Economic Sociology tat the  秋葵视频. Both are graduates and alumni 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. Organisational Network Analysis with xUCINET in R

Instructor: Bruce Cronin

About:
This course provides an introduction to social network analysis applied to the study of organisational networks. These social networks are shaped and influenced by organisational tasks and structures and various methods of accounting for these effects are considered in the course. The course also builds on elementary understanding  of the UCINET software package by examining how  many repetitive analytical tasks, common in organisational network analysis,  can be  automated  using the new R-based version of the  software, xUCINET.

By the end of this course participants will be able to:

• confidently execute UCINET commands in RStudio;
• write simple scripts to execute and repeat a series of SNA tasks
• import organisational network data from a variety of formats  and export results in various formats
• analyse a variety of inter-organisational relationships appropriately
• isolate and analyse organisation-specific effects on social interactions
• customise network visualisations

Requirements
Participants should have an elementary understanding of Social Network Analysis. No previous experience with UCINET software or programming is expected.

Instructor
Bruce Cronin is Professor of Economic Sociology at the 秋葵视频, where he is co-director of the Networks and Urban Systems Centre

General references
Borgatti, SP, Everett, MG, Johnson, JC, and Agneessens, F. (2022) Analysing Social Networks Using R. London: Sage.

3.  Topic Modelling for Literature Reviews

Instructor: Quang (James) Duong

About:
With the proliferation of academic databases, manual thematic/content analysis is no longer effective to cover all papers and extract common topics. To cope with that issue, machine learning-based topic modelling is a well-known technique to explore prominent topics from a big collection of texts. This course aims to provide a basic knowledge about text pre-processing and an introduction of the most common topic model – Latent Dirichlet Allocation (LDA) using the Python-programming language. The participants will have an opportunity to practise on real academic dataset.

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

• holistically diagnose the sources of noises and challenges from unstructured abstract data.
• design a customised pipeline of text processing methods to address the noise and produce a ready-to-use collection of documents (i.e., corpus).
• employ topic modelling for identifying the prevailing themes in your research domain.
• visualise the extracted topics on a semantic space.

Requirements:
Participants should have an elementary knowledge of the Python-programming language.

Instructor
Dr  Quang (James) Duong  is a  lecturer in  Business Operations  at the University of  Greenwich. He is a graduate and alumnus of the  Networks  and Urban Systems Centre.

4. Social Media and Text Mining in R

Instructor: Mu Yang

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

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

• make use of key metrics used for analysing social media,
• undertake sentiment analysis on user-generated-content on social media,
• employ topic modelling for identifying trends in social data

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

Instructor
Dr Mu Yang is Senior Lecturer in Business Analytics at the Birkbeck, University of London, where she is Programme Director for the MSc in Business Analytics.

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

Instructors: J眉rgen Lerner and Alessandro Lomi

About:
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 ().

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

Requirements:
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.

Instructors:
J眉rgen 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.

6.  R and GitHub for Research

Instructor: Matthew Smith

About:
The workshop provides an introduction to the R programming language for those without any previous experience with this or as a refresher if you haven’t used it for a while. It will introduce the tidyverse – a set of functions and packages for data processing, cleaning and visualisation in R.

It will then outline how to efficiently store R code for further development and reuse, using the GitHub platform. This will cover version control, R package development and sharing code and development with research collaborators. We will also provide details on how to turn your analysis into high quality documents using RMarkdown.

By the end of the session participants should be able to:

• Use R and RStudio
• Make use of the tidyverse for data processing and visualisation
• Use GitHub & GitHub desktop to share and collaborate on R projects

The goal of the course is to provide participants with an overview of how to use R for research – including data processing and visualisation, and how the GitHub platform can be utilised. More specifically, how GitHub can be used for collaboration and for version control on research projects, and as a repository for R code.

Requirements:
No prior knowledge of R or GitHub is required as an introduction will be provided.

Instructor:
Dr Matthew Smith is a Lecturer in Strategy at Edinburgh Napier University. A graduate and alumnus of the Networks and Urban Systems Centre, Dr Smith has extensive experience in R applications of social network analysis.

Fees

Early Bird offer ends on Friday 20 May at 5pm

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

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

2. Organisational Network Analysis  with xUCINET in R:

• General  拢240  (Early Bird 拢200)
• Student  拢160 (Early Bird 拢120)

3. Topic Modelling for Literature Reviews:

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

4. Social Media and Text Mining in R:

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

5. Introduction to Relational Event Modelling:

• General  拢240  (Early Bird 拢200)
• Student  拢160 (Early Bird 拢120)

6. R and GitHub for Research

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

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