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复杂系统与复杂性科学  2024, Vol. 21 Issue (2): 38-44    DOI: 10.13306/j.1672-3813.2024.02.005
  复杂网络 本期目录 | 过刊浏览 | 高级检索 |
有向加权网络的重要模体识别及其应用
侯喜妹, 王高峡, 杨帆, 王怡珂
三峡大学 a.理学院;b.数学研究中心,湖北 宜昌 443002
Identification of Important Motifs in Directed Weighted Networks and Its Application
HOU Ximei, WANG Gaoxia, YANG Fan, WANG Yike
a. College of Science; b. Mathematics Research Center, China Three Gorges University ,Yichang 443002, China
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摘要 为识别有向加权网络中的重要加权模体,采用边权定性为强弱标签的方式将有向加权网络转换为标签网络、简单模体拓展至标签模体。对于三节点的标签模体类型,用模体在随机网络中出现相应次数的概率估计值代替模体遍历的含时过程,引入与标签模体类型相关联的动态指标识别出有向加权网络中的重要标签模体。将其应用到中国篮球职业联赛(CBA)2019—2020赛季总决赛广东队、辽宁队的传球网络,获得球队在比赛中出现的重要传球模式及构成相应传球模式的重要球员。重要标签模体的识别对挖掘有向加权网络的重要构建模式、关键节点有着显著作用。
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侯喜妹
王高峡
杨帆
王怡珂
关键词 有向加权网络标签网络标签模体篮球传球网络运动表现分析    
Abstract:In order to identify the important weighted motifs in the directed weighted networks, the directed weighted networks are transformed into label networks and the simple motifs are expanded to label motifs by defining the edge weights as strong and weak labels. For the label motifs of the three nodes, the time-consuming procedure of subgraph traversal is replaced by the estimated probability of the corresponding number of the motifs appear in the random networks, and the important label motifs in the directed weighted networks are identified by introducing a dynamic indicator associated with the label motif type. It is applied to the passing networks of Guangdong team and Liaoning team in the 2019—2020 finals of China Basketball Association (CBA). The important passing modes of the teams in the games and the important players in the corresponding modes are obtained. The important label motifs play a significant role in mining the important construction patterns and key nodes of the directed weighted networks.
Key wordsdirected weighted networks    label networks    label motifs    basketball passing networks    sports performance analysis
收稿日期: 2022-09-26      出版日期: 2024-07-17
ZTFLH:  TP391  
  N94  
基金资助:宜昌市大学科学研究与应用项目(A21-3-018)
通讯作者: 王高峡(1969-),女,湖北秭归人,博士,教授,主要研究方向为复杂网络理论及其应用。   
作者简介: 第一作者: 侯喜妹(1997-),女,安徽亳州人,硕士研究生,主要研究方向为复杂网络结构性质的研究。
引用本文:   
侯喜妹, 王高峡, 杨帆, 王怡珂. 有向加权网络的重要模体识别及其应用[J]. 复杂系统与复杂性科学, 2024, 21(2): 38-44.
HOU Ximei, WANG Gaoxia, YANG Fan, WANG Yike. Identification of Important Motifs in Directed Weighted Networks and Its Application[J]. Complex Systems and Complexity Science, 2024, 21(2): 38-44.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2024.02.005      或      https://fzkx.qdu.edu.cn/CN/Y2024/V21/I2/38
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