Gravity model for dyadic Olympic competitions

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Abstract

In the Olympic Games, professional athletes representing their nations compete regardless of economic, political and cultural differences. In this study, we apply gravity model to observe characteristics, represented by distances among nations that directly compete against one another in the Summer Olympics. We use dyadic data consisting of medal winning nations in the Olympic Games from 1952 to 2016. To compare how the dynamics changed during and after the Cold War period, we partitioned our data into two time periods (1952-1988 and 1992-2016). Our research is distinguishable from previous studies in that we newly introduce application of gravity model in observing the dynamics of the Olympic Games. Our results show that for the entire study period, countries that engaged each other in competition in the finals of an Olympic event tend to be similar in economic size. After the Cold War, country pairs that compete more frequently tend to be similar in genetic origin.

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Is Open Access

Modularity and community structure in networks

M. Newman (2006)

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.