{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T20:58:00Z","timestamp":1768078680010,"version":"3.49.0"},"reference-count":65,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":143,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Plan","award":["2020YFC2007003"],"award-info":[{"award-number":["2020YFC2007003"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>In this study, we sorted out the research hotspots in sports science by bibliometric method and also used social network analysis to explore the relationship between knowledge networks and their scientific performance. We found 38 high\u2010frequency keywords with obvious curricular nature or classical direction of sports science research and 4 high\u2010frequency research groups. The topics of hotspots covered the secondary disciplines of sports science: physical education and training, national traditional sports, sports human science, and sports humanities and sociology. However, sports human science research is less; therefore, accelerating the research of sports human science is the focus of future research. Meanwhile, we use social network structure analysis (i.e., centrality, clustering coefficient, PageRank, and structural holes) to study the relationship between knowledge elements in knowledge networks and their scientific performance. In addition to betweenness centrality, the closeness centrality, clustering coefficient, and structural holes of knowledge elements are significantly and positively related to their influence. In the relationship between knowledge elements and productivity, betweenness centrality and closeness centrality show significant positive correlations, and clustering coefficient and structural hole show significant negative correlations. Therefore, knowledge networks can be used to predict the scientific performance of knowledge elements.<\/jats:p>","DOI":"10.1155\/2021\/9981202","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T19:35:09Z","timestamp":1621884909000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The Hotspots of Sports Science and the Effects of Knowledge Network on Scientific Performance Based on Bibliometrics and Social Network Analysis"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5814-9196","authenticated-orcid":false,"given":"Linxiao","family":"Ma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8424-1156","authenticated-orcid":false,"given":"Yuzhu","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2568-4894","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5012-0324","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3526-6568","authenticated-orcid":false,"given":"Sai-Fu","family":"Fung","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-4117","authenticated-orcid":false,"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8400-6298","authenticated-orcid":false,"given":"Qian","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1167742"},{"key":"e_1_2_11_2_2","first-page":"117","article-title":"Research on the visual analysis method of topic domain research hotspots tracking and trend forecasting","volume":"40","author":"Chen Y.","year":"2017","journal-title":"Information Theory and Practice"},{"key":"e_1_2_11_3_2","first-page":"109","article-title":"Dynamic analysis of China\u2019s circular economy research from the perspective of knowledge mapping","volume":"32","author":"Luo F.","year":"2017","journal-title":"Journal of Statistics and Information"},{"key":"e_1_2_11_4_2","first-page":"156","article-title":"Research on knowledge management based on word frequency analysis in recent 10 years","volume":"32","author":"Chu J.","year":"2014","journal-title":"Information Science"},{"key":"e_1_2_11_5_2","first-page":"19","article-title":"Hotspots and future trends of physical education research in China in recent five years","volume":"53","author":"Li Y.","year":"2019","journal-title":"Journal of Wuhan Institute of Physical Education"},{"key":"e_1_2_11_6_2","first-page":"229","article-title":"Key words analysis of sports humanistic sociology papers based on CSSCI","volume":"35","author":"Jiang L.","year":"2014","journal-title":"Journal of Southwest University for Nationalities (Humanities and Social Sciences Edition)"},{"key":"e_1_2_11_7_2","volume-title":"Understanding Scientific Literatures: A Bibliometric Approach","author":"Donohue J. 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