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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
5R01HD075712-02
Application #
8726456
Study Section
Special Emphasis Panel (ZRG1-RPHB-A (51))
Program Officer
Newcomer, Susan
Project Start
2013-09-01
Project End
2018-12-31
Budget Start
2015-01-01
Budget End
2015-12-31
Support Year
2
Fiscal Year
2015
Total Cost
$322,122
Indirect Cost
$52,114
Name
Duke University
Department
Type
Organized Research Units
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Heroy, Samuel; Taylor, Dane; Shi, F Bill et al. (2018) RIGID GRAPH COMPRESSION: MOTIF-BASED RIGIDITY ANALYSIS FOR DISORDERED FIBER NETWORKS. Multiscale Model Simul 16:1283-1304
Li, Zichao; Mucha, Peter J; Taylor, Dane (2018) NETWORK-ENSEMBLE COMPARISONS WITH STOCHASTIC REWIRING AND VON NEUMANN ENTROPY. SIAM J Appl Math 78:897-920
Lee, Hsuan-Wei; Malik, Nishant; Mucha, Peter J (2018) Evolutionary prisoner's dilemma games coevolving on adaptive networks. J Complex Netw 6:1-23
Trinh, Sarah L; Lee, Jaemin; Halpern, Carolyn T et al. (2018) Our Buddies, Ourselves: The Role of Sexual Homophily in Adolescent Friendship Networks. Child Dev :
Hill, L M; Moody, J; Gottfredson, N C et al. (2018) Peer norms moderate the association between mental health and sexual risk behaviors among young men living in Dar es Salaam, Tanzania. Soc Sci Med 196:77-85
Granell, Clara; Mucha, Peter J (2018) Epidemic spreading in localized environments with recurrent mobility patterns. Phys Rev E 97:052302
Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J (2017) Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks. Phys Rev X 7:
Weir, William H; Emmons, Scott; Gibson, Ryan et al. (2017) Post-Processing Partitions to Identify Domains of Modularity Optimization. Algorithms 10:
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
Shi, Ying; Moody, James (2017) Most Likely to Succeed: Long-Run Returns to Adolescent Popularity. Soc Curr 4:13-33

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