Most existing work including studies by Sayed and Zhang used

Most existing work, including studies by Sayed and Zhang, used some variation of degree centrality to evaluate network-based interventions. As Valente (2010) points out, a major limitation of in-degree centrality is that nominations received may be redundant. Borgatti (2006) showed that conventional measures of centrality are inadequate for finding nodes optimally positioned to spread information for enos inhibitor or to be removed from a network to disrupt spread. We address this by using another alternative to degree centrality finding that utilization of social network structures using other approaches can lead to higher population impacts. One additional strength deserves special comment. We are among the first to incorporate a well-established machine learning method called influence maximization to widen the range of network-based strategies. Influence maximization techniques try to find an optimal number of structural nodes, which, if activated, would cause the spread of intervention to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic (Morone & Makse, 2015). The idea of influence maximization was initially introduced in the context of viral marketing (Richardson & Domingos, 2002), and was used in other fields like applied physics (Altarelli, Braunstein, Dall’Asta, Wakeling, & Zecchina, 2014). However, it has not received much attention in obesity research. Sangachin et al. (2014) presented a model for the spread of obesity interventions in a networked population, and compared their method against a greedy-based approach that follows an influence maximization strategy. For the influence maximization method, no more data other than the network structure is needed in practice. The need to know the structure of the existing social network is common among all network-based targeting methods, including centrality-based methods. We are limited in our ability to generate agent networks with full fidelity to reality given that our agents are “sampled” independently. Therefore, the degree of clustering and homophily is likely to be less than the real world in which human networks operate. We generated networks that match the real world only in the macro-sense of total numbers of ties. The implication of this is that our ability to discern the comparative advantages of network based targeting are likely to be an under-estimate of the true marginal utility of network-based approaches. Moreover, the NLSY79 dataset was chosen to test our experiments on a realistic population. We acknowledge that using real-world distributions of body-weights over time for model validation (as in Fig. 2) is not ideal and does not indicate that our model is able to provide insights into the causal mechanisms generating these changes. It is possible that we are able to recreate overall BMI distribution changes but have the underlying mechanisms wrong. However, given the absence of longitudinal data on weight trajectories and social network histories, this approach at least allows us to identify a badly performing model. In supplemental materials, we show that consistent results are obtained when we perform our experiments on simulated populations based on the HERITAGE family study dataset (Jackson, Stanforth, Gagnon, Rankinen, & Leon, 2002). Lastly, it is noted that the question of whether there is diffusion in obesity related behaviors is a matter of some controversy. Our results are premised on the idea that there are network diffusion processes at play.
Acknowledgements The authors would like to thank Professor Takeru Igusa and Dr. Claudia Nau for their help on this work. Research reported in this publication was supported by the Global Obesity Prevention Center (GOPC) at Johns Hopkins University, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and the Office of the Director, National Institutes of Health (OD) under award number U54HD070725. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.