2022 Social Networks and Health Workshop
The 2022 training will be virtual, asynchronous, and open to all, with Foundational and Intermediate offerings. Training materials will be posted here as they are available.
The schedule for the Q&A session for registered attendees is below, followed by Foundational then Intermediate training offerings which are available to everyone.
Q&A SCHEDULE for REGISTERED ATTENDEES (MAY 10-12, 2022)
| May 10th | May 11th | May 12th | |||
| Start | End | Foundations | Advanced | Applied | Substantive Talks |
| 10:00 | 10:30 | Points, Lines & Metrics (Moody) | 2-mode SOAM (Schaefer) | Community Engagement(Molina, Mulawa) | Brea Perry (aging)
Ben Cornwell (SNA & Widowhood) Ashton Verdery (kinship models) |
| 10:30 | 11:00 | Time Heterogeneity in SOAM (McMillan) | |||
| 11:00 | 11:30 | Open problems (various) | Community Detection (Mucha) | Family & Aging (Perry, Cornwell, Verdery) | |
| 11:30 | 12:00 | Ego-Networks Q&A (DNAC) | Genetics & Epi (Pasquale) | ||
| 12:00 | 12:30 | Ethics (Adams) | Missing data/Imputation (Smith) | Interventions (Valente, Kennedy) | |
| 12:30 | 13:00 | BREAK | BREAK | ||
| 13:00 | 13:30 | BREAK | |||
| 13:30 | 14:00 | Diffusion & Peer Influence (Moody) |
|
||
| 14:00 | 14:30 | IDEA Net (Moody) | Relational Event Models (Duxbury) | ||
| 14:30 | 15:00 | Stats for Nets: Intro (Moody) | Network TMLE (Zivich) | ||
| 15:00 | 15:30 | Lecture from: Stats for Nets: ERGM (Moody) | Community Aware Models (Volfovsky) | ||
| 15:30 | 16:00 |
If the schedule block for a session contains a link, it is highly recommended for participants to engage with the lecture and/or materials before attending the Q&A section. Clicking the link will place the appropriate module at the top of your browser.
May 10-12, 2022, 11:00am-4:00pm. All times in U.S. Eastern Time (UTC-04:00).
Lecturers and Research Assistants for the 2022 Social Networks and Health Workshop:
James Moody, Director of Duke Network Analysis Center and Professor of Sociology, Duke University
Bernard Coles IV, Department of Sociology, Duke University
Benjamin Cornwell, Department of Sociology, Cornell University
Scott Duxbury, Department of Sociology, University of North Carolina at Chapel Hill
David Kennedy, RAND Corporation
Cassie McMillan, Northeastern University
Yamilé Molina, Division of Community Health Sciences, University of Illinois at Chicago
Jon Morgan, Department of Sociology, Duke University
Peter Mucha, Department of Mathematics, Dartmouth College
Marta Mulawa, Duke University School of Nursing and Duke Global Health Institute
Dana Pasquale, Department of Population Health Sciences, Duke University
Brea Perry, Indiana University
Joseph Quinn, Department of Sociology, Duke University
Craig Rawlings, Department of Sociology, Duke University
David Schaefer, University of California at Irvine
Jeffrey Smith, Department of Sociology, University of Nebraska-Lincoln
Liann Tucker, Department of Sociology, Duke University
Tom Valente, Department of Preventive Medicine, University of Southern California
Gabriel Varela, Department of Sociology, Duke University
Ashton Verdery, Department of Sociology and Criminology, Penn State
Tom Wolff, Department of Sociology, Duke University
Paul Zivich, Causal Inference Research Lab, University of North Carolina at Chapel Hill
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
- Introduction to Social Networks and Health (Jim Moody, Duke University)
- Lab: Network Data Cleaning (Maria Cristina Ramos Flor, Duke University)
- Network Data Collection (Craig Rawlings, Duke University)
- 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)
- Network Visualization and Communities Lab (Jim Moody, Duke University)
- Statistical Models for Networks (Jim Moody, Duke University)
- Peer Influence and Network Diffusion (Jim Moody, 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)
- 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)
- 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)
- 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)
- 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)
- HIV (Marta Mulawa, Duke University)
- 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.