The Centre for Networks and Enterprise Excellence (CNEE) of the Edinburgh Business School, is hosting a virtual workshop on “Introduction to Social Network Analysis with R and statnet” on Wednesday 12th January 2022, from 9:00-13:00 (UK time).
The workshop is free to attend and all are welcome, so please circulate to anyone who may be interested. Please sign up for this workshop on our Eventbrite page. You will then be sent instructions to join the session via Microsoft Teams.
Introduction to Social Network Analysis with R and statnet
Instructor: Dr Lorien Jasny (University of Exeter)
Wednesday 12th January 2021, 09:00-13:00 (UK time)
This workshop session will serve as an introduction to the importation, manipulation, and descriptive analysis of social network data within the R/statnet platform. Topics covered will include: an overview of basic R functions and data types; importation of network data into R; network data manipulation; management of metadata for complex networks; visualization of network data; calculation of network descriptives (e.g., centrality scores, graph-level indices); and use of classical network analytic techniques (e.g., blockmodeling), and related packages like igraph.
No prior experience with R or statnet is assumed, but attendees should have familiarity with the basic concepts of descriptive network analysis, however we will discuss each concept and review some applications.
statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac). statnet packages can be used to handle a wide range of simulation and analysis tasks, including support for large networks, statistical network models, network dynamics, and missing data.