Introduction to Social Network Analysis 2025

Starts May 15, 2025

SHORT COURSE DESCRIPTION

Social networks are defined by a set of relationships among a group of individuals and are common in any discipline in which individuals interact. Examples include friendship among a group of people, collaborations among employees in an organization and co-authorship among researchers in a field. Analyzing how and why individuals interact can help researchers better understand both the system as a whole as well as how these interactions impact systemic change.

The purpose of this two-day course is to introduce methods for analyzing social network data, focusing on the types of network data common in the social sciences. Although some exploratory and descriptive methods in social network analysis will be covered, the focus of this course is to teach participates how to fit and interpret social network models.

This course is targeted for participants interested in learning about social network models and is appropriate for researchers at any stage in their career, students included. We recommend that participants be familiar with R and fitting statistical models although previous experience with social network analysis is not necessary.

The course will begin with a brief introduction to R for participants who are new to that software. We will then cover descriptive methods and practice visualizing/exploring networks in R. Much of the course will focus on social network models, social selection models in particular. We will also cover multilevel social network models and discuss goodness of fit. Throughout the course, we will incorporate hands-on practice, analyzing real world data and fitting models in R.

After completing the course, participants will have an understanding of quantitative methods available for analyzing social networks as well as the current state of model capabilities. They will be able to analyze their own network data using R code created during the course; they will be able to both fit and interpret a variety of social network models; and they will have an understanding of model assessment and goodness of fit.

DATES AND TIMES

May 15-16, 2025 (Thur-Fri)

10am-5pm Eastern Daylight Time (UTC-4)

The instructor will determine timing of lunch break, as well as morning and afternoon breaks.

COURSE FEES

Professional: $375

Full-time student*: $195

*Full-time students need to submit student status proof at https://go.umd.edu/CILVR-Student-24 to request a discount code prior to registration.

*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM-24.

HOW TO REGISTER

Prior to registration, participants not affiliated with UMD need to get a valid UMD associate account in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details about the UMD associate account, please click here.

For UMD affiliated participants, you may register using your existing UMD directory ID.

To request the promotional code prior to registration:

- Full-time students can submit the student status proof at https://go.umd.edu/CILVR-Student-24 to request a student discount code prior to registration. Note that it may take 2-3 business days for your request to be processed.

- HDQM department registrants can request the HDQM discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM-24. Note that it may take 2-3 business days for your request to be processed.

TOPICS

Introduction to R
Introduction to Social Network Analysis
Descriptive statistics and community detection
Introduction to Social Network Models
Conditionally Independent Dyad Models
Latent Space Models
Stochastic Block Models
Goodness of fit
Hierarchical Latent Space Models

TARGET AUDIENCE

Graduate students, faculty, research professionals who are interested in social networks.

REQUISITE KNOWLEDGE

It is assumed that participants have knowledge of general and generalized linear models, especially logistic regression. It is recommended that participants be familiar with R or other command line software.

SOFTWARE

Empirical examples and hands-on exercises for this workshop will be done using the open source software R. It is recommended that participants attend the course having downloaded the most recent version of R on their laptop.

LOCATION AND PLATFORM

· The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/).

· This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).

· Within a limited time, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.

IMPORTANT COURSE DETAILS

Platform: Participants who are not affiliated with UMD need to get a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.

Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.

Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.

Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).

Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.

Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.

THE INSTRUCTOR

Dr. Tracy Sweet is an Associate Professor of Measurement, Statistics, and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland. Dr. Sweet has taught several workshops on social network models and recently taught an advanced graduate course on social network models. She has also taught a number of graduate-level quantitative methods courses on general linear models, multilevel modeling, Bayesian inference, and statistical consulting. Her research interests include developing statistical models for social networks as well as data mining methods for multilevel social network data. Dr. Sweet has published methodological papers in prominent journals such as Journal of Educational and Behavioral Statistics and Social Networks, and application papers in American Educational Research Journal, Sociology of Education, and Educational Psychologist. Her research has been funded by the Institute of Educational Sciences (IES). She currently serves as an Associate Editor for Journal of Educational and Behavioral Statistics. Before joining the EDMS faculty in the fall of 2013, Dr. Sweet worked as a postdoctoral researcher at Carnegie Mellon University where she received her Ph.D. in 2012. She may be reached at tsweet@umd.edu.

REFUND POLICY

Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

CONTACT

For any further questions, please contact Ashani Jayasekera at sna.cilvr@gmail.com.

To request a copy of the payment receipt, please contact the OES office at oes-finance@umd.edu.

CILVR Short Course Series

Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)

CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Measurement, Statistics and Evaluation (EDMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. EDMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.

Full course description