Diffusion is a primary way in which social networks affect health, both directly, as when sexually transmitted diseases are passed between partners, and indirectly, as when norms about health relevant behavior travel through a social network. While crucial, with the exception of a few well specified and comparatively simple cases, the complexity of most network diffusion problems make analytic models cumbersome or intractable. As such, social simulations become an effective tool for understanding how network features affect population health, but we lack a generalized simulation framework, making it difficult to compare work across investigative teams. Thus, the primary aim of this project is to build a flexible network diffusion simulation system that accurately portrays dynamic, multi-relational social networks. The project makes use of newly- developed multi-slice network structures as a flexible tool for extending networks from static single-relation graphs to complex, evolving systems composed of multiple relations that can easily accommodate complex transmission rules. The fundamental insight of this proposal is twofold. Theoretically, it rests on the notion that complexity emerges naturally from the connections among simpler sub-systems. Methodologically, we operationalize linking of simpler sub-systems to a complex whole by using multi-slice network models. Thus, we first identify a generalized diffusion framework resting on differences in network structure along three dimensions and differences in dyadic transmission processes, also varying along three dimensions. Combined, these 6 complexity dimensions allow us to specify a network diffusion model that ranges from extremely simple (certain spread of a fixed pathogen over a static network) to complex (negotiated ideational diffusion across multiple evolving networks). The resulting simulation system will give health researchers across multiple domains the ability to model underlying network diffusion processes and answer clear questions about how features of the network structure affect health and health disparities.
At both the individual and population levels, health is affected by networks through a process of physical (as with infection spread) or social (as with peer influence) diffusion. Since analytic models for the vast majority of complex network diffusion situations are lacking, this project develops a generalized network simulation framework for health-relevant diffusion that can accommodate a wide variety of relevant situations. The result of this project is increased capacity to model the links between network structure, individual behavior, and the health status of individuals and communities.
