This is a core introductory course to Network Analysis for Social Sciences by Mitchell Centre for Social Network Analysis – a leading cross-disciplinary research group in the development and application of social network analysis techniques, located in the School of Social Sciences at the University of Manchester.

The course covers the main network-analytical concepts, methods, and data collection/processing techniques for social sciences. The course is hands-on, offering participants to experience analysis of real social network data to discuss their own projects in network analysis with relevant experts from the Mitchell Centre.

The course is largely based around the use of UCINET point-and-click software and is broadly accessible. No prior knowledge of network analysis or quantitative methods is assumed for this course.

This course offers an in-depth overview of concepts and measures in social network analysis. The course is run by network experts at the Mitchell Centre for Social Network Analysis – a leading cross-disciplinary research group in the development and application of social network analysis techniques, located in the School of Social Sciences at the University of Manchester.

Amongst the topics covered are:
* Introduction to R and R packages for SNA
· Importing network data in R
· Basic manipulation of network data and visualization
· Data collection
· Multiplex and signed networks
· Missing data
· Equivalence and blockmodeling

This course offers an in-depth overview of statistical models for social network analysis. The course is run by network experts at the Mitchell Centre for Social Network Analysis – a leading cross-disciplinary research group in the development and application of social network analysis techniques, located in the School of Social Sciences at the University of Manchester.

The course covers some of the core statistical methods, including Exponential Random Graph Models (ERGMs), Stochastic Actor-Oriented Models (SAOMs/SIENA) and Relational Event Models (REMs). In addition, we will cover tERGMs, autoregressive models (ALAAMs) and ERGMs/SAOMs for two/mode and multi groups (multilevel). The last day participants will be able to choose between advanced longitudinal network models, or semantic and socio-semantic network modelling with automap and mpnet.

For most of the analysis, we will be using R (aswell as MPNet). No prior knowledge of R is required, but basic knowledge of social network analysis and quantitative methods is recommended (see also the course in the first week of the Summer School).