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

Huang, Qian; Fung, Sai-fu; Liu, Bin; Zhao, Ningshe; Zhang, Zhiyong; Wang, Qiurong; Xiao-li, Zhang; Xia, Yang; Ge, Xiaoyu; Ding, Jianlan

doi:10.1109/access.2019.2960559

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…e online social support network is based on the Van der Poole social support classification method, and it is divided into social online support, emotional online support, achievement online support, and practical online support. Based on the Institute of Population and Development of Xi'an Jiaotong University's social network of migrant workers and the social network of athletes under Huang Qian's complex network, the online social discussion network is analysed [23,31] from four aspects: employment online discussion, revenue online discussion, professional online discussion, and love and marriage online discussion. e questionnaire data are collected by positioning method.…”

Section: Elements Of Athletes' Online Social Structurementioning

confidence: 99%

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

et al. 2021

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In response to the lack of research on the online social network structure of athletes, elements of research on the online social network structure of athletes were constructed based on the whole network perspective and through the study of the characteristics of the whole online social network structure of athletes, in order to provide reference for the physical and mental health development of athletes from a new perspective. Data were collected through questionnaires, and several software programs were used to preprocess and analyse the collected data. Through the analysis of the online whole network structure, it was found that the network density of the online support network was generally greater than that of the online discussion network, and athletes still showed stronger practical support demands and behaved more rationally in the process of training and learning life, while from the perspective of the relationship structure, the athletes’ family and classmates’ online support is weaker than that in previous studies; in terms of the whole network, strong relationships still dominate in this population, while attention should be paid to the impact of weak relationships.

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Section: Elements Of Athletes' Online Social Structurementioning

confidence: 99%

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

et al. 2021

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“…From the phenomenon point of view, this accompaniment is determined by the performance of the principal nodes interacting with each other, but from the microanalysis, it is determined by the attributes. For a long time, we do not pay enough attention to the analysis of node properties [23][24][25][26][27][28]. However, the behavior of the principal node depends on its properties.…”

Section: Social Network Representation Based On Valence Bond Theorymentioning

confidence: 99%

Athlete Social Support Network Modeling Based on Modern Valence Bond Theory

Zhao

2020

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Based on the Valence Bond theory, an attempt is proposed to the complex network. The principle of chemical bonding of the basic particles that make up the substance creates a metaphor between the formation of social networks. By analyzing the integration of atoms by relying on the chemical bonds between particles, then the social basis for the connection between social network nodes should depend on the tangible or intangible attribute resources that characterize social capital around the main node. Based on the above analysis, the social node is divided into active nodes and passive nodes, and a dynamic model of social network formation is proposed, the Valence Bond model of social network. Through this model, the actual athlete group nodes are depicted, and the representation of the model and the evolution of network structure are given with the actual data.

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“…Additionally, they are combined with Machine Learning (ML) techniques, to exploit knowledge, update it and to infer new relationships between data. On the other hand, (Huang, et. al., 2019) indicates the use of ML as a means to study the relationship between SNSs entities.…”

Section: Introductionmentioning

confidence: 99%

Education and New Developments 2022 – Volume I

2022

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This book contains the full text of papers and posters presented at the International Conference on Education and New Developments (END 2022), organized by the World Institute for Advanced Research and Science (WIARS).Education, in our contemporary world, is a right since we are born. Every experience has a formative effect on the constitution of the human being, in the way one thinks, feels and acts. One of the most important contributions resides in what and how we learn through the improvement of educational processes, both in formal and informal settings. The International Conference seeks to provide some answers and explore the processes, actions, challenges and outcomes of learning, teaching and human development. The goal is to offer a worldwide connection between teachers, students, researchers and lecturers, from a wide range of academic fields, interested in exploring and giving their contribution in educational issues. We take pride in having been able to connect and bring together academics, scholars, practitioners and others interested in a field that is fertile in new perspectives, ideas and knowledge.We counted on an extensive variety of contributors and presenters, which can supplement our view of the human essence and behavior, showing the impact of their different personal, academic and cultural experiences. This is, certainly, one of the reasons we have many nationalities and cultures represented, inspiring multi-disciplinary collaborative links, fomenting intellectual encounter and development.END 2022 received 790 submissions, from more than 45 different countries, reviewed by a double-blind process. Submissions were prepared to take form of Oral Presentations, Posters, Virtual Presentations and Workshops. The conference accepted for presentation 263 submissions (33% acceptance rate), from which, 233 submissions are published in full text in these volumes.

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

Cited by 5 publications

References 22 publications

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

Analysis of the Structural Characteristics of the Online Social Network of Chinese Professional Athletes

Athlete Social Support Network Modeling Based on Modern Valence Bond Theory

Education and New Developments 2022 – Volume I

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