Since many decades power functions are well-known in counting single scientists or co-author pairs in social networks. However, in this paper a developed procedure for visualizing a bivariate distribution of co-author...Since many decades power functions are well-known in counting single scientists or co-author pairs in social networks. However, in this paper a developed procedure for visualizing a bivariate distribution of co-author pairs’ frequencies hence producing three-dimensional graphs is presented. This distribution is explained by a fundamental principle of social group formation and described by a mathematical model. This model is applied to 52 co-authorship networks. For 96% of them the squared multiple R is larger than 0.98 and for 77% of the 52 networks even larger than 0.99. The visualized social Gestalts in form of three-dimensional graphs are rather identically with the corresponding empirical distributions. Question: Can we expect a general validity of this mathematical model for co-authorship networks?展开更多
文摘Since many decades power functions are well-known in counting single scientists or co-author pairs in social networks. However, in this paper a developed procedure for visualizing a bivariate distribution of co-author pairs’ frequencies hence producing three-dimensional graphs is presented. This distribution is explained by a fundamental principle of social group formation and described by a mathematical model. This model is applied to 52 co-authorship networks. For 96% of them the squared multiple R is larger than 0.98 and for 77% of the 52 networks even larger than 0.99. The visualized social Gestalts in form of three-dimensional graphs are rather identically with the corresponding empirical distributions. Question: Can we expect a general validity of this mathematical model for co-authorship networks?
