Lugano, Switzerland

Using Social Network Analysis to Understand Data

Social network analysis is used to understand communities by investigating their structure. How individuals in communities are connected to one another can influence information flows, actor importance, and the overall behaviour of the community. Social network analysis allows us to identify key actors, hierarchies of relationships, brokers, groups that act in a coordinated way, patterns of information flow, and the resilience of the community as a whole.

Networks are nodes (individuals) that are connected with other nodes by links (or edges). In social network analysis, the nodes in a network are usually people. More broadly, nodes can be used to represent almost anything, such as cities, brands, online communities, scientific articles, political organizations, colours in paintings, emotions, historical events, or words in a language. This means that network analysis can be used to unlock and understand many kinds of data.

In this workshop, students will learn the basic concepts of social network analysis and extend its use to network analysis more broadly, including data analysis and network visualization. Students will learn the material in a practical hands-on fashion, largely using R.

If students have ongoing projects of their own, they will be able to investigate these and gain new insights into their own research. By the end of the workshop, students will have a vocabulary for understanding network analysis and should have the knowledge needed to understand most of the research in network analysis that they are likely to see in the social sciences.

Students will learn concepts like small world analysis (how structured is the network?), homophily (do similar nodes cluster together?), network closure (are nodes in the network in harmony with one another?), distance (how far away are objects in the network from one another?), clustering and community detection (do communities develop?), and centrality (are some nodes more important than others?).

Course leader

Thomas Hills is the Director of the Behavioural and Data Science MSc and the Bridges Doctoral Training Centre in Mathematical and Social Sciences at the University of Warwick, UK.

Target group

graduate students, doctoral researchers, early career researchers, experienced researchers

Prerequisites

Students taking this workshop should have at least basic experience in R or another programming language. There are a number of free or inexpensive online courses well worth the investment in time (e.g., Datacamp) that offer introductory courses in R that are sufficient prerequisites for this course. Students may also prepare by taking the Introduction to R and Rstudio at the Summer School (10-11 August, online, free of charge if attending this course). A general introductory book to statistics in R will also work. Though the course will primarily use R, I will provide all the code. Therefore, this course can be a way to improve your R skills as well.

Credits info

The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you.

Fee info

CHF 700: Reduced fee: 700 Swiss Francs per weekly workshop for students (requires proof of student status).
CHF 1100: Normal fee: 1100 Swiss Francs per weekly workshop for all others.