Social Networks and Health 2025
Registration: https://events.duke.edu/SNH-2025
Stream Links:
COMING SOON! Schedule currently reflect SN&H 2024
Lecturers and RAs:
James Moody, Director of Duke Network Analysis Center and Professor of Sociology, Duke University
Tom Wolff, Department of Sociology, Duke University
Madelynn Wellons, Department of Sociology, Duke University
Gabriel Varela, Department of Sociology, Duke University
Dana Pasquale, Department of Population Health Sciences, Duke University
Past Workshops:
Training Modules:
User guide for the following training modules: The training modules are split into two categories. “Foundational” training covers essential aspects of network analysis and dispenses the necessary knowledge for understanding the “intermediate” training modules. Each training module can contain up to three types of links: a lecture video, a Q&A video, and a link to a google drive folder. The google drive folder contains all the scripts, data, slides, and additional material provided by the lecturer. Additional links to particular files may also be added.
If you find that a script requires a piece of data that is not present in that folder, try checking in our miscellaneous folder. If you find another problem with the script and/or data, feel free to get in touch with one of the research assistants in the workshop or email snh.dnac@gmail.com with your inquiry.
Foundational
- Preparatory Work
- Software and Code Guide
- Preparation Code – Installing Libraries.R (as text file)
- test lab please run this ahead of time if you have doubts about your R set up
- Pajek walk-through
- Social Networks in R Review (Liann Tucker, RTI)
- Introduction to Social Networks and Health (Jim Moody, Duke University)
- Intro Statistics on Networks (Jim Moody, Duke University)
- Lecture
- Lab: Network Data Cleaning (Maria Cristina Ramos Flor, Duke University)
- Network Data Collection (Craig Rawlings, Duke University)
- Modalities and Pragmatics in Data Collection (Jimi Adams, University of Colorado Denver)
- Ethics in Social Networks Research (Jimi Adams, University of Colorado Denver)
- Whole Network Descriptive Statistics (Molly Copeland, Duke University)
- Egocentric Network Analysis (Brea Perry, Indiana University)
- Network Visualization (Jim Moody, Duke University)
- Community Detection (Peter Mucha, Dartmouth College)
- Proper Community Detection via CHAMP (Peter Much, Dartmouth College)
- Network Visualization and Communities Lab (Jim Moody, Duke University)
- What’s the best community detection method? (Jim Moody, Duke University)
- Statistical Models for Networks (Jim Moody, Duke University)
- Peer Influence and Network Diffusion (Jim Moody, Duke University)
- Peer influence (Craig Rawlings, Duke University)
- Lab: Diffusion, ERGM, and Stats (Brian Aronson, Duke University)
- Introduction to Stochastic Actor Oriented Models (SAOM / SIENA) (David Schaefer, UC Irvine)
- Ethics: Network Data IRB and Data Security Issues (AddHealth Data as example) (Kathleen Mullan Harris, UNC-Chapel Hill)
- Data Archiving (Dana Pasquale, Duke University)
- IDEANet (Gabriel Varela, Kieran Lele, Duke University)
- Linguistic Variation and Social Networks (Robin Dodsworth, NC State University)
- Field Experiments (Sharique Hassan, Duke University)
- Social Balance (Craig Rawlings, Duke University)
- Lab Experiments (Ashley Harrell, Duke University)
- Recommending Readings
Intermediate
- Workshop Welcome (Jim Moody and DNAC RAs, Duke University)
- Time Heterogeneity in Stochastic Actor-Oriented Models (Cassie McMillan, Northeastern University)
- 2-mode SAOM (David Schaefer, University of California, Irvine)
- Network TMLE (Paul Zivich, University of North Carolina at Chapel Hill)
- Lecture
- Code, and documentation
- Relational Event Models (Scott Duxbury, UNC-Chapel Hill)
- Network Imputation (Jeffrey Smith, University Nebraska-Lincoln)
- Genetics and Epi (Dana Pasquale, Duke University)
- Egonetwork Data Collection (Brea Perry, Indiana University)
- Network Data Collection with Network Canvas (Michelle Birkett, Northwestern University)
- Positional Analysis in Networks (Jim Moody, Duke University)
- Strategies and Cautionary Tales with Very Large Networks (Jon Morgan and Jim Moody, Duke University)
- Community Detection in R in 2021 and Beyond (Peter Mucha, Dartmouth College)
- Lecture (Part 1)
- Lecture (Part 2)
- Q&A
- All data, code, and slides (the code lab “CommunitiesSNH2022.Rmd” is most up to date)
- Missing Data and Bias (Jon Morgan, Duke University)
- Missing Data (Jim Moody, Duke University)
- A Primer on Inference with Partially Observed Network Data (Tyler McCormick, University of Washington)
- Bi-partite ERGM (David Hunter and Alina R. Kuvelkar, Penn State University)
- ERGM for Ego Networks and Bipartite Networks (Thomas Wolff, Northwestern University)
- New Features of ERGM 4.0 (Jim Moody, Duke University)
- Stochastic Actor-Oriented Model Advanced Techniques I: Asymmetric Peer Influence and Simulations (David Schaefer, UC Irvine, and Cassie McMillan, Northeastern University)
- Meta-Analysis of SIENA Stochastic Actor-Oriented Model Estimates (Daniel Ragan, University of New Mexico – Albuquerque)
- Agent-Based Models (ABM) and Diffusion Simulation Models (Jim Moody, Duke University)
- Community Aware Models (Alexander Volfovsky, Duke University)
- Ethics and Human Subjects Considerations for Link Tracing Designs (Dana Pasquale, Duke University)
- Relational Block Models
- Latent Spaces and Causal Effects (Alex Volfovsky, Duke University)
- Wearables for Data Collection (Peter Cho, Duke University)
- EpiModel ~ Epidemic Disease Model Simulation (Sam Jenness, Emory University)
- In Silico Network Experiments to Probe Mechanisms and Inform Study Design (Carter Butts, University of California, Irvine)
- Workshop Close and Future Directions (Jim Moody, Duke University)
Substantive talks
- Aging (Brea Perry, Indiana University)
- Network Interventions (Thomas Valente, University of Southern California)
- Network Measurement in Community Engagement Interventions (Yamilé Molina, University of Illinois Chicago)
- Lecture (audio only, follow along with slides)
- All data, code, and slides
- Sociogram Interventions (David Kennedy, RAND corporation)
- Social Networks and Widowhood (Benjamin Cornwell, Cornell University)
- Kinship Networks and Health (Ashton Verdery, Pennsylvania State University)
- Simulating Kinship Networks (Ashton Verdery, Pennsylvania State University)
- RDS/Link Data (Ashton Verdery, Pennsylvania State University)
- HIV (Marta Mulawa, Duke University)
- Network HIV Prevention Interventions with youth in Tanzania (Nina Yamanis, American University)
- Network interventions show the value of community investment (Yamile Molina, University of Illinois)
- Network methods for behavior change (Tom Valente, Johns Hopkins University)
Research Design Lab
- Katherine Abbott
- Chen-Shuo Hong
- Lindsay Xu
- Alison Comfort
- Jacqueline M. Kent-Marvick
- Caroline V. Brooks
- Joshua Awua
- Lauren Newmyer
Funding for the Social Networks and Health workshop is provided by NIH grant R25HD079352.
