# Network Analysis

• What are Networks?
• The first lecture is designed to familiarize the learners with the idea of networks. First, networks are highly visual, so the lecture introduces the network graphs. Then, we talk about what is social network analysis (SNA), the role of networks in our lives, and applications of SNA in a variety of settings. Finally, we talk about network theory in organizations and application of SNA in organization. A session in R is dedicated to demonstrating networks.
• Network Analysis as a Method
• This lecture introduces all the foundational network concepts. We start with important terminology, then move to network study design, data collection and descriptive statistics. Then, we examine everything learned in the lecture on a real-life dataset. R session has several segments: loading and manipulating network data, drawing graphs using different packages, interpreting graphs.
• Foundational Network Measures
• This lecture starts with analyzing networks on the very basic units: dyads and triads. We learn how to interpret triadic census. Then, we move on to one of the most important concepts in network analysis: centrality. We explore these foundational network measures on a real-life dataset. R sessions are dedicated to local analyses (dyads, triads and other measures) and calculating centrality measures.
• Social Influence Models
• In this lecture, we introduce the idea of modeling on networks. The idea is simple in principle: we use the network measures, which we’ve learned in previous lectures, as predictors in regular statistical models, such as regression. First, we discuss the theories of social influence. Then, we discuss how social influence models are built. We discuss best practices in social influence network models and apply them to a real-life dataset in an R session.
• Social Selection Modeling
• This lecture takes network analysis to a next level – the models of social selection. We start by talking about the very idea of statistical network models – why do we need them? Then, we talk about social processes and the theory behind network formation. Next we discuss the role of random graphs in the analytic process, which leads us to exponential random graph models (or the models of social selection). We discuss how to build ERGMs and apply this knowledge to a real-life dataset.
• Community Detection Approaches
• One of the most important application of network analysis is community detection. We start by talking about communities: what are they? Then, we discuss various approaches to community detection and look at a network-level method: blockmodels. We discuss theory in blockmodeling, roles and positions, and learn how to build blockmodels in R on a real-life dataset.
• Project Assignment