Using Social Network Analysis to Understand Data
Lecturer: Thomas Hills
Modality: In presence
Week 1: 14-18 August 2023
Workshop Contents and Objectives
Social network analysis is used to understand communities by investigating their structure. How individuals in communities are connected to one another can influence information flows, actor importance, and the overall behaviour of the community. Social network analysis allows us to identify key actors, hierarchies of relationships, brokers, groups that act in a coordinated way, patterns of information flow, and the resilience of the community as a whole.
Networks are nodes (individuals) that are connected with other nodes by links (or edges). In social network analysis, the nodes in a network are usually people. More broadly, nodes can be used to represent almost anything, such as cities, brands, online communities, scientific articles, political organizations, colours in paintings, emotions, historical events, or words in a language. This means that network analysis can be used to unlock and understand many kinds of data.
In this workshop, students will learn the basic concepts of social network analysis and extend its use to network analysis more broadly, including data analysis and network visualization. Students will learn the material in a practical hands-on fashion, largely using R.
If students have ongoing projects of their own, they will be able to investigate these and gain new insights into their own research. By the end of the workshop, students will have a vocabulary for understanding network analysis and should have the knowledge needed to understand most of the research in network analysis that they are likely to see in the social sciences.
Students will learn concepts like small world analysis (how structured is the network?), homophily (do similar nodes cluster together?), network closure (are nodes in the network in harmony with one another?), distance (how far away are objects in the network from one another?), clustering and community detection (do communities develop?), and centrality (are some nodes more important than others?).
Students taking this workshop should have at least basic experience in R or another programming language. There are a number of free or inexpensive online courses well worth the investment in time (e.g., Datacamp) that offer introductory courses in R that are sufficient prerequisites for this course. A general introductory book to statistics in R will also work. Though the course will primarily use R, I will provide all the code. Therefore, this course can be a way to improve your R skills as well.
Baek, E. C., Porter, M. A., & Parkinson, C. (2021). Social network analysis for social neuroscientists. Social Cognitive and Affective Neuroscience, 16(8), 883-901.
Hills, T. T., & Kenett, Y. N. (2021). Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science. Topics in Cognitive Science.
Kim, J., & Hastak, M. (2018). Social network analysis: Characteristics of online social networks after a disaster. International Journal of Information Management, 38(1), 86-96.