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.
|Keister, Lisa A; Benton, Richard; Moody, James (2016) Lifestyles through Expenditures: A Case-Based Approach to Saving. Sociol Sci 3:650-684|
|Skardal, Per Sebastian; Taylor, Dane; Sun, Jie et al. (2016) Erosion of synchronization: Coupling heterogeneity and network structure. Physica D 323-324:40-48|
|Malik, Nishant; Bookhagen, Bodo; Mucha, Peter J (2016) Spatiotemporal patterns and trends of Indian monsoonal rainfall extremes. Geophys Res Lett 43:1710-1717|
|Skardal, Per Sebastian; Taylor, Dane; Sun, Jie (2016) Optimal synchronization of directed complex networks. Chaos 26:094807|
|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|
|Moody, James; Benton, Richard A (2016) Interdependent effects of cohesion and concurrency for epidemic potential. Ann Epidemiol 26:241-8|
|Skardal, Per Sebastian; Taylor, Dane; Sun, Jie et al. (2016) Collective frequency variation in network synchronization and reverse PageRank. Phys Rev E 93:042314|
|Taylor, Dane; Shai, Saray; Stanley, Natalie et al. (2016) Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation. Phys Rev Lett 116:228301|
|Verdery, Ashton M; Merli, M Giovanna; Moody, James et al. (2015) Brief Report: Respondent-driven Sampling Estimators Under Real and Theoretical Recruitment Conditions of Female Sex Workers in China. Epidemiology 26:661-5|
|Merli, M Giovanna; Moody, James; Smith, Jeffrey et al. (2015) Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling. Soc Sci Med 125:79-93|
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