Social Network Analysis and Visualization with R

Social network analysis benefits greatly from computational tools, and the R statistical language is a suitable environment to describe, visualise, and analyse network datasets of various sizes.

Intensive learning across disciplines is happening in social network analysis and network science. This course makes new ideas from many disciplines – statistical physics, ecology, computer science – available to the social scientist, through libraries in R, and via the interactive possibilities of R notebooks available in R studio. We will use libraries such as network, sna and igraph.

The course is a good choice for those who have not taken the first-week course Social Networks: Theoretically Informed Analysis with UCINET, because we will briefly revisit the main concepts covered there. In this case, having an awareness of the basic concepts of social network analysis would be a plus.

The course is also a good choice for those who did take first-week UCINET course, because it adds scalability of the R framework not present in UCINET to analyse larger networks, many networks, or fork the analytic strategy to various alternative methods and indices. The course also adds multiple approaches to hypothesis testing and comparisons to random baseline null hypotheses, sophisticated visualisations, and a broader range of algorithm choice. We add outlook to methods from network science not present in UCINET (such as the replication of the Watts-Strogatz small world rewiring experiment).

Day 1
We introduce R and programming basics, data import, and network descriptives. We discuss object types and programming constructs relevant to network analysis. We explore various ways to import data to R, and learn about the generation of random graphs with diverse methods. We learn about various data structures – sociomatrices, edgelists, nodelists – and ways to transform data among these formats. We will import data from various sources – Excel sheets, CSV and other delimited text formats, UCINET and other special data formats.

Day 2
We learn what makes social networks distinctive: a strong tendency to closure and positive degree correlation (assortativity). We will compare human social networks to animal social networks (and to decidedly not social network systems) to test the idea that animal social networks are indeed social. We will discuss various centrality measures, and centralisation at the graph level. We will compare centrality measures, and discuss the theoretical imagery behind these measures, with detailed explanation of the formulas for the various indices.

Day 3
We introduce measures for brokerage (structural autonomy), and various brokerage roles at the boundaries of groups. We use rewirings to generate the random baseline expectation for the relative frequency of brokerage roles, and diagnose an organisational network of a thousand workers to detect bottlenecks in information flow in between divisions.

Day 4
We look at blocklmodelling and cohesive community detection, with an emphasis on goodness of fit measures, and comparisons with random baselines. We will compare several methods of community detection, and discuss underlying assumptions about the nature of network data and processes of tie formation.

Day 5
Dedicated to advanced visualisations of network data. We discuss layout algorithms, and parameter choices for these – for small and large graphs. We also introduce node density heat maps for extremely large graphs. We learn to convey information on graphs using node size and colour, edge size and colour. We discuss graph creation and export for various purposes: research article, PowerPoint, poster, interactive online presentation.

Essential Basic knowledge of statistics and probability (probability density functions, tests of statistical significance, ordinary least squares estimator), and of data entry and manipulation (using Excel sheets, CSV, TXT data formats). 

Advantageous, but not essential Basic knowledge of R (objects, vectors, matrices) and awareness of basic concepts in programming (for and while loops, if-else conditional statements).

These basics will be covered briefly, to the extent that enables independent learning.