The goal of this study was to understand research trends and collaboration patterns together with scholarly impact within the domain of global obesity research. We developed and analysed bibliographic affiliation data collected from 117,340 research articles indexed in Scopus database on the topic of obesity and published from 1993-2012. We found steady growth and an exponential increase of publication numbers. Research output in global obesity research roughly doubled each 5 years, with almost 80% of the publications and authors from the second decade (2003-2012). The highest publication output was from the USA - 42% of publications had at least one author from the USA. Many US institutions also ranked highly in terms of research output and collaboration. Fifteen of the top-20 institutions in terms of publication output were from the USA; however, several European and Japanese research institutions ranked more highly in terms of average citations per paper. The majority of obesity research and collaboration has been confined to developed countries although developing countries have showed higher growth in recent times, e.g. the publication ratio between 2003-2012 and 1993-2002 for developing regions was much higher than that of developed regions (9:1 vs. 4:1). We also identified around 42 broad disciplines from authors' affiliation data, and these showed strong collaboration between them. Overall, this study provides one of the most comprehensive longitudinal bibliometric analyses of obesity research. This should help in understanding research trends, spatial density, collaboration patterns and the complex multi-disciplinary nature of research in the obesity domain.
The time interval between two snapshots is referred to as the window size. A given longitudinal network can be analysed from various actor-level perspectives, such as exploring how actors change their degree centrality values or participation statistics over time. Determining the optimal window size for the analysis of a given longitudinal network from different actor-level perspectives is a well-researched network science problem. Many researchers have attempted to develop a solution to this problem by considering different approaches; however, to date, no comprehensive and well-acknowledged solution that can be applied to various longitudinal networks has been found. We propose a novel approach to this problem that involves determining the correct window size when a given longitudinal network is analysed from different actor-level perspectives. The approach is based on the concept of actor-level dynamicity, which captures variability in the structural behaviours of actors in a given longitudinal network. The approach is applied to four real-world, variable-sized longitudinal networks to determine their optimal window sizes. The optimal window length for each network, determined using the approach proposed in this paper, is further evaluated via time series and data mining methods to validate its optimality. Implications of this approach are discussed in this article.
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