POS 6729 Political Networks

Politcal Network Analysis (theory and applications) POS 6729 is a graduate level course taught face to face. Taught currently and every other year since 2018 at UCF

Course Description

Network theory dates its conceptual origins back to the 1730s thanks to Euler (1736); but the starting point of modern graph theory is considered to be the work of two Hungarian mathematicians on random graphs: Erdos and Reny in 1959 and 1960. This course is an introduction to network theory. The course will focus on introducing concepts, metrics and applications in network analysis with a focus on policy networks, collaborations and interdependent systems. The course will also provide students with the ability to devise their own network survey, collect data, and to do their own network analysis in Python, via the NetworkX package. The course is thus divided into a general lecture in which concepts and metrics are explained to students via standard lecture format, and a technical lecture in which students will familiarize themselves with the analysis of Networks via NetworkX. The course will also include introductory lectures on how to write a script. This latter is an important transferable skill as scripts are used in many programs used in today world and academic environments: from SPSS, to STATA, from Matlab to R. The topics covered will be the following:

• General concepts: Networks, classes of networks, network metrics, exponential random graph models, multiplex networks, diffusion on networks. Applying network theoretical tools for the analysis of policy and political networks as well as integrated socialecological networks.
• Technical skills: Survey instrument for devising networks; Python NetworkX package
• Optional skills (to be discussed with the instructor): Integrating Networks and computational modelling, using R (sna, igraph, and ergm packages are great).

To facilitate learning, the course is divided into four main blocks:

1. The first block of the course will introduce what are networks, different network classes and the most common network metrics used to analyze networks.
2. The second block of the course will focus on (best) practices to collect data for network analysis, and application of network analysis to real world problems.
3. The third block will focus on advanced network topics (exponential random graph models and multiplex networks).
4. The final block of the course will be centered on group and individual final projects. We will work in class on defining meaningful research questions related to network analysis, how to gather relevant network data, analyze networks and interpret the results. Optional topics can be discussed with the instructor and addressed in this third block.

Student Learning Outcomes

Students that successfully complete this class will be able to:

• Define and understand key concepts in network science
• Use NetworkX package in Python to analyze networks
• Apply theory and methods learned to interpret and analyze problems in their area of interest