This workshop is intended for relative newcomers to social network analysis. The workshop will provide an introduction to social network data collection with an emphasis on social survey methods. The workshop will consider a variety of related methodological issues such as research design, measurement, sampling, data analysis, and ethics, as well as the linkage of these issues to data collection. Different types of data collection techniques will be illustrated such as the name generator, position generator, and name roster. The different opportunities and constraints associated with data collection for whole versus ego-networks will be considered. Some discussion of non-survey techniques may also be provided. Some attention may also be given to mixed methods.

The ACT (Activate, Connect, Transform) model aims to guide the design, implementation, and evaluation of social and collaborative interventions that Activate, Connect, and Transform individuals, organizations, health systems, and communities. The ACT model responds to pressing needs in healthcare and community action: how to meaningfully engage patients in decision-making, research, and policy; how to leverage social networks for the dissemination and implementation of high-quality innovations; and how to create networks of learning and improvement in healthcare and community settings. This is essential in our rapidly changing landscapes, where bridging formal and informal social networks and relations can enhance outcomes and quality of services and equip health and social systems to respond dynamically to emerging needs and crises. The three pillars of ACT involve: Activate: empowering individuals, organizations, and communities with the motivation, skills, and strategies to mobilize resources and foster relationships. Connect: Building and nurturing supportive relationships, networks of influence and knowledge sharing, and partnerships among individuals, teams, and communities to strengthen collective capacity and achieve shared goals. Transform: Driving improvement in behaviors, processes, and outcomes by implementing and sustaining evidence-based innovations. The workshop Agenda: - Introduction to the three-pillar approach - Activate interventions: •“Network diagnostics”/charting at the individual or community levels to provide network actors with a bird’s eye view of their existing networks and potentials for further activation.(Yousefi Nooraie, et al., 2021) •Asset mapping - Connect interventions: •Strategies to facilitate connectivity and optimize social structure, following the framework developed by Yousefi Nooraie, et al. (2021) - Transform interventions: •Strategies to enhance the dissemination and implementation of valued interventions using networking strategies, following the framework developed by Bunger and Yousefi Nooraie, et al. (2023) - Cyclic approach to intervention refinement - A quick introduction to evaluation •Approaches to assess network evolution, social activation, and resilience building, with an emphasis on mixed-methods analysis (Yousefi Nooraie et al., 2020) With this unique three-pillar approach—Activate, Connect, and Transform—the ACT model aims to inform the design of interventions to build dynamic, resilient, and inclusive networks where individuals are engaged, networks are optimized for knowledge sharing and support, and dynamically respond to emergent needs. Workshop length: 3 hours Maximum number of attendees: 20 References: Bunger, A. C., Yousefi-Nooraie, R., Warren, K., Cao, Q., Dadgostar, P., & Bustos, T. E. (2023). Developing a typology of network alteration strategies for implementation: a scoping review and iterative synthesis. Implementation Science, 18(1), 10. Yousefi Nooraie, R., Mohile, S. G., Yilmaz, S., Bauer, J., & Epstein, R. M. (2021). Social networks of older patients with advanced cancer: Potential contributions of an integrated mixed methods network analysis. Journal of geriatric oncology, 12(5), 855-859. Yousefi Nooraie, R., Sale, J. E., Marin, A., & Ross, L. E. (2020). Social network analysis: An example of fusion between quantitative and qualitative methods. Journal of Mixed Methods Research, 14(1), 110-124. Yousefi Nooraie, R., Warren, K., Juckett, L. A., Cao, Q. A., Bunger, A. C., & Patak-Pietrafesa, M. A. (2021). Individual-and group-level network-building interventions to address social isolation and loneliness: a scoping review with implications for COVID19. PloS one, 16(6), e0253734.

In large networks with thousands or millions of actors, the interactions among actors are not affected by the interactions among all other actors, because many social networks are more local than global in nature: Indeed, actors may not even know most other actors, and therefore cannot be influenced by them. A simple class of models that respects the local nature of many social networks assumes that actors are divided into communities and that actors are affected by other actors of the same community, but are not affected by actors outside of the community. The communities may be known or unknown. If the communities are unknown, one can infer the unobserved communities from the observed social network along with the social forces that govern interactions among actors within and between communities. The proposed workshop focuses on next-generation ERGMs for large networks implemented in R package bigergm, which is an evolution of R packages hergm and lighthergm. The workshop will introduce the basic ideas of next-generation ERGMs and will demonstrate them by examples. Participants will be provided with sample R scripts. Software: Fritz, Schweinberger, Komatsu, Dahbura, Nishida, and Mele (2024). R package bigergm. https://cran.r-project.org/web/packages/bigergm/index.html Literature: The basic idea is introduced in Schweinberger and Handcock (2015). Local dependence in random graph models: Characterization, properties and statistical inference. Journal of the Royal Statistical Society, Series B, 77, 647-676. An application to systemic risk in social networks can be found in Fritz, Georg, Mele and Schweinberger (2024). Vulnerability webs: Systemic risk in software networks. Computational details are provided in Babkin, Stewart, Long, and Schweinberger (2020). Large-scale estimation of random graph models with local dependence. Computational Statistics & Data Analysis, 152, 1-19.