A Dynamic Network Analysis approach for evaluating knowledge dissemination in a multi-disciplinary collaboration network in obesity research

Effective knowledge dissemination is important to promote the adoption of new concepts and tools. This study aims to provide a framework that assesses strategies for successful knowledge dissemination in a research collaboration network. We propose a Markov-chain Monte Carlo (MCMC) approach along with Dynamic Network Analysis (DNA) to model a social network and understand how different knowledge dissemination strategies can be used in a research collaboration network. The proposed method was demonstrated through a case study that uses a multi-disciplinary collaboration network in obesity research at an academic medical center. To assess the impact of initial disseminators on knowledge dissemination, four different strategies were considered. The simulation results indicated that the best strategy to disseminate knowledge within this obesity research network may be to use central agents in clusters when considering the coverage and speed of knowledge dissemination.

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