Health and health behavior depend crucially on diffusion processes over social networks; both the spread of communicable diseases, such as HIV, and the diffusion of beliefs and practices that shape health behavior, such as dietary risk behavior, can be understood as generalized diffusion over (sometimes very complex and dynamic) social networks. However, despite the clear need, few of our formal analytic tools can be used to model diffusion over any but the simplest of these systems, as the mathematics for modeling diffusion over dynamic multi- relational networks are currently intractable except by direct simulation; which is often a weak foundation for generalizable knowledge; we provide a novel solution to modeling complex diffusion processes over dynamic networks by decomposing the networks into a set of tractable subsets and relation specific transmission rules. We will develop improved methods for network community detection that specifically targets understanding diffusion, and include such communities in our general framework; then test these new models against a bank of constructed and real social networks to assess model performance. Using simulation, novel new network clustering techniques and extensions of formal diffusion models, this project will develop the tools necessary to interpolate between the simplest direct diffusion cases and the most complex social behavior cases; advances will provide fundamental insights into health- relevant diffusion, yielding more robust modeling and highlighting directions for possible intervention.
A wide variety of health outcomes depend on network diffusion; this is clear in cases of direct disease transmission through intimate networks (HPV, HIV, etc.), but also underlies behavioral changes relevant for health, such as dietary practices or knowledge of substance-use risks. We propose a new class of simulation and formal modeling techniques that will allow researchers to understand which how network features affect diffusion in large dynamic networks. Such understanding is necessary to understand health disparities across demographic sub-groups and to build effective diffusion interventions that can protect public health.
|Edelmann, Achim; Moody, James; Light, Ryan (2017) Disparate foundations of scientists' policy positions on contentious biomedical research. Proc Natl Acad Sci U S A 114:6262-6267|
|Weir, William H; Emmons, Scott; Gibson, Ryan et al. (2017) Post-Processing Partitions to Identify Domains of Modularity Optimization. Algorithms 10:|
|Shi, Ying; Moody, James (2017) Most Likely to Succeed: Long-Run Returns to Adolescent Popularity. Soc Curr 4:13-33|
|Copeland, Molly; Bartlett, Bryce; Fisher, Jacob C (2017) Dynamic Associations of Network Isolation and Smoking Behavior. Netw Sci (Camb Univ Press) 5:257-277|
|Taylor, Dane; Myers, Sean A; Clauset, Aaron et al. (2017) EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS. Multiscale Model Simul 15:537-574|
|Fisher, Jacob C (2017) Exit, cohesion, and consensus: social psychological moderators of consensus among adolescent peer groups. Soc Curr 5:49-66|
|Smith, Jeffrey A; Moody, James; Morgan, Jonathan (2017) Network sampling coverage II: The effect of non-random missing data on network measurement. Soc Networks 48:78-99|
|Stanley, Natalie; Shai, Saray; Taylor, Dane et al. (2016) Clustering network layers with the strata multilayer stochastic block model. IEEE Trans Netw Sci Eng 3:95-105|
|Keister, Lisa A; Benton, Richard; Moody, James (2016) Lifestyles through Expenditures: A Case-Based Approach to Saving. Sociol Sci 3:650-684|
|Taylor, Dane; Skardal, Per Sebastian; Sun, Jie (2016) SYNCHRONIZATION OF HETEROGENEOUS OSCILLATORS UNDER NETWORK MODIFICATIONS: PERTURBATION AND OPTIMIZATION OF THE SYNCHRONY ALIGNMENT FUNCTION. SIAM J Appl Math 76:1984-2008|
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