Efficient Strategy Mining for Football Social Network
With the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. How...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8823189 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849412558076772352 |
|---|---|
| author | Taige Zhao Ningning Cui Yunliang Chen Man Li |
| author_facet | Taige Zhao Ningning Cui Yunliang Chen Man Li |
| author_sort | Taige Zhao |
| collection | DOAJ |
| description | With the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. However, most of the existing works utilize a simplex strategy to improve certain indicators in the team and do not consider the effect of strategy adjustment based on the current situation. In this paper, we study the problem of efficient strategy mining in football social network. To address this problem, we propose a quantitative way to combine the aspects of coordination, adaptability, flexibility, and tempo into a passing network, which notably improves the timeliness and information content of the existing network. On this basis, we design a suppression function to express the impact of strategy. Then, we propose a novel passing network and group cooperation scheme based on quantified team performance to obtain the efficient strategies. At last, the experimental results show that, based on the performance of the same team, our optimized passing network has a higher winning rate in practice. |
| format | Article |
| id | doaj-art-647859aa5cf042329a5f4f598f20b27f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-647859aa5cf042329a5f4f598f20b27f2025-08-20T03:34:25ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88231898823189Efficient Strategy Mining for Football Social NetworkTaige Zhao0Ningning Cui1Yunliang Chen2Man Li3School of Computer Science, University of Sydney, Sydney NSW 2017, AustraliaSchool of Computer Science, Anhui University, Hefei 230601, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Information Technology, Deakin University, Geelong VIC 3220, AustraliaWith the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. However, most of the existing works utilize a simplex strategy to improve certain indicators in the team and do not consider the effect of strategy adjustment based on the current situation. In this paper, we study the problem of efficient strategy mining in football social network. To address this problem, we propose a quantitative way to combine the aspects of coordination, adaptability, flexibility, and tempo into a passing network, which notably improves the timeliness and information content of the existing network. On this basis, we design a suppression function to express the impact of strategy. Then, we propose a novel passing network and group cooperation scheme based on quantified team performance to obtain the efficient strategies. At last, the experimental results show that, based on the performance of the same team, our optimized passing network has a higher winning rate in practice.http://dx.doi.org/10.1155/2020/8823189 |
| spellingShingle | Taige Zhao Ningning Cui Yunliang Chen Man Li Efficient Strategy Mining for Football Social Network Complexity |
| title | Efficient Strategy Mining for Football Social Network |
| title_full | Efficient Strategy Mining for Football Social Network |
| title_fullStr | Efficient Strategy Mining for Football Social Network |
| title_full_unstemmed | Efficient Strategy Mining for Football Social Network |
| title_short | Efficient Strategy Mining for Football Social Network |
| title_sort | efficient strategy mining for football social network |
| url | http://dx.doi.org/10.1155/2020/8823189 |
| work_keys_str_mv | AT taigezhao efficientstrategyminingforfootballsocialnetwork AT ningningcui efficientstrategyminingforfootballsocialnetwork AT yunliangchen efficientstrategyminingforfootballsocialnetwork AT manli efficientstrategyminingforfootballsocialnetwork |