Modeling for Professional Athletes’ Social Networks Based on Statistical Machine Learning

Professional athletes' social networks have rarely been explored. In this paper, we propose a framework to analyze athletes' social networks. Based on the characteristics of athletes' social network structure and social support theory, the matrix distribution is introduced to describe the network structure. The observation model of the Bayesian network is established, and then the Gaussian process analysis model of sparse matrix is used to investigated the network. We collected real-world data of athletes' social networks by questionnaires, which contain eight thematic network data. With our method, the interpersonal network of professional athletes is analyzed and the adjacency relationships are predicted. Finally, taking the social subnet of the athlete social network as an example and using the model and algorithm, the node support factor analysis and the complex network community convergence factors are analyzed. We found that professional athletes' social networks have a stronger small-world characteristic than the general public's social networks. The proposed model and algorithm provide a new quantitative approach for studying professional athletes' social networks.