Social Network Analysis: From the Basics to Advanced Models in One Week - online
11/09/2023 - 15/09/2023
University of Glasgow
Michael T. Heaney
Intermediate (some prior knowledge)
NCRM Centre Manager
The concept of “social networks” is increasingly a part of social discussion, organizational strategy, and academic research. The rising interest in social networks has been coupled with a proliferation of widely available network data, but there has not been a concomitant increase in understanding how to analyse social network data.
This course presents concepts and methods applicable for the analysis of a wide range of social networks, such as those based on family ties, business collaboration, political alliances, and social media. Classical statistical analysis is premised on the assumption that observations are sampled independently of one another. In the case of social networks, however, observations are not independent of one another, but are dependent on the structure of the social network. The dependence of observations on one another is a feature of the data, rather than a nuisance.
This course is an introduction to statistical models that attempt to understand this feature as both a cause and an effect of social processes. Since network data are generated in a different way than many other kinds of social data, the course begins by considering the research designs, sampling strategies, and data formats that are commonly associated with network analysis. A key aspect of performing network analysis is describing various elements of the network’s structure. To this end, the course covers the calculation of a variety of descriptive statistics on networks, such as density, centralization, centrality, connectedness, reciprocity, and transitivity. We consider various ways of visualizing networks, including multidimensional scaling and spring embedding. We learn methods of estimating regressions in which network ties are the dependent variable, including the quadratic assignment procedure and exponential random graph models (ERGMs). We consider extensions of ERGMs, including models for two-mode data and networks over time.
Instruction is split between lectures and hands-on computer exercises. Students may find it to their advantage to bring with them a social network data set that is relevant to their research interests, but doing so is not required. The instructor will provide data sets necessary for completing the course exercises.
The course covers:
- Network theory
- Research design for network data
- Descriptive statistics for networks
- Exponential random graph models (ERGM)
- Extensions of ERGMs for two-mode and time-series data
By the end of the course participants will:
- Develop network theories and hypotheses
- Prepare network data for analysis in R
- Estimate statistical models of social networks
- Present the results of social network analysis
Prerequisite knowledge for the course includes the fundamentals of probability and statistics, especially hypothesis testing and regression analysis. This course assumes that students can interpret the results of Ordinary Least Squares, Probit, and Logit regressions. They should also be familiar with the problems that are most common in regression, such as multicollinearity, heteroscedasticity, and endogeneity. Finally, students should be comfortable working with computers and data. No prior knowledge of R or network analysis is required.
IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the latest version of R Statistical software. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.
The course will run online from 11 September 2023 to 15 September 2023 from 9am to 12pm (Noon) and from 1pm to 3pm during each of the five days of the course.
MONDAY 11 SEPTEMBER 2023
Welcome, course procedures, requirements, and objectives
Lecture 01: Introduction to social network analysis
Lecture 02: Major theories
Lecture 03: Research designs and data
TUESDAY 12 SEPTEMBER 2023
Computer Exercises 01: Introduction to Network Analysis in R
Lecture 04: Descriptive statistics
Computer Exercises 02: Descriptive statistics
Lecture 05: Inferential network analysis
WEDNESDAY 13 SEPTEMBER 2023
Lecture 06: Exponential Random Graph Models (ERGMs)
Computer Exercises 03: Exponential Random Graph Models (ERGMs)
THURSDAY 14 SEPTEMBER 2023
Lecture 07: Temporal Exponential Random Graph Models (TERGMs)
Computer Exercises 04: Temporal Exponential Random Graph Models
Individual consultations. Participants should plan to work in the evening to refine their presentations for Friday morning.
Friday 15 SEPTEMBER 2023
Lecture 08: Generalized Exponential Random Graph Models (GERGMs)
Computer Exercises 05: Generalized Exponential Random Graph Models (GERGMs)
The fee per teaching day is:• £30 per day for registered students• £60 per day for staff at academic institutions, Research Councils researchers, public sector staff, staff at registered charity organisations and recognised research institutions. • £100 per day for all other participants. In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date.If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of the cancellation of a course. The University of Southampton’s Online Store T&Cs also continue to apply.
Website and registration:
Descriptive Statistics, Statistical Theory and Methods of Inference, Regression Methods, Social Network Analysis, Quantitative Software
Related publications and presentations:
Statistical Theory and Methods of Inference
Social Network Analysis