Please wait a minute...
文章检索
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
全文: PDF(2079 KB)  
输出: BibTeX | EndNote (RIS)      
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。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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
[1]MILO R, SHEN-ORR S, ITZKOVITZ S, et al. Network motifs: simple building blocks of complex networks[J]. Science, 2002, 298(5594): 824-827.
[2]SPORNS O, KÖTTER R, FRISTON K J. Motifs in brain networks[J]. PLoS Biology, 2004, 2(11): e369.
[3]BENSON A R, GLEICH D F, LESKOVEC J. Higher-order organization of complex networks[J]. Science, 2016, 353(6295): 163-166.
[4]SHEN-ORR S S, MILO R, MANGAN S, et al. Network motifs in the transcriptional regulation network of Escherichia coli[J]. Nature Genetics, 2002, 31(1): 64-68.
[5]DOBRIN R, BEG Q K, BARABÁSI A L, et al. Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network[J]. BMC Bioinformatics, 2004, 5(1): 1-8.
[6]JIN Y, WEI Y, XIU C, et al. Study on structural characteristics of China′s passenger airline network based on network motifs analysis[J]. Sustainability, 2019, 11(9): 2484.
[7]贾承丰, 韩华, 完颜娟, 等. 基于网络模体特征攻击的网络抗毁性研究[J]. 复杂系统与复杂性科学, 2019, 14(4): 43-50.
JIA C F, HAN H, WAN Y J, et al. Network destruction resistance based on network motif feature[J]. Complex Systems and Complexity Science, 2019, 14(4): 43-50.
[8]LIU H, XU X, LU J A, et al. Optimizing pinning control of complex dynamical networks based on spectral properties of grounded Laplacian matrices[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 51(2): 786-796.
[9]刘慧, 王炳珺, 陆君安, 等. 复杂网络牵制控制优化选点算法及节点组重要性排序[J]. 物理学报, 2021, 70(5): 284.
LIU H, WANG B J, LU J A, et al. Node-set importance and optimization algorithm of nodes selection in complex networks based on pinning control[J]. Acta Physica Sinica, 2021, 70(5): 284.
[10] ONNELA J P, SARAMÄKI J, KERTÉSZ J, et al. Intensity and coherence of motifs in weighted complex networks[J]. Physical Review E, 2005, 71(6): 065103.
[11] CHOOBDAR S, RIBEIRO P, SILVA F. Motif mining in weighted networks[C]∥2012 IEEE 12th International Conference on Data Mining Workshops. Brussels, Belgium: IEEE, 2012: 210-217.
[12] LI J, YANG D, JI C. Mine weighted network motifs via Bayes′ Theorem[C]∥2017 4th International Conference on Systems and Informatics (ICSAI). Hangzhou, China: IEEE, 2017: 448-452.
[13] LI J, LV P, JI C. Uncover product review patterns via weighted motifs[C]∥2018 5th International Conference on Systems and Informatics (ICSAI). Nanjing, China: IEEE, 2018: 445-448.
[14] LI J, YANG D, LV P. Visualize classic play′s composing patterns: a weighted motif mining framework[J]. Multimedia Tools and Applications, 2019, 78(5): 5989-6012.
[15] SHEN X, GONG X, JIANG X, et al. High-order organization of weighted microbial interaction network[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid, Spain: IEEE, 2018: 206-209.
[16] PICCIOLO F, RUZZENENTI F, HOLME P, et al. Weighted network motifs as random walk patterns[J]. New Journal of Physics, 2022, 24(5): 053056.
[17] 张岩, 杨改生. 社会网络分析在团队运动表现中的应用:研究综述[J]. 中国体育科技, 2022, 58(1): 100-108.
ZHANG Y, YANG G S. The application of social network analysis in team sports performance:a review[J]. China Sport Science and Technology, 2022, 58(1): 100-108.
[18] HÅLAND E M, WIIG A S, HVATTUM L M, et al. Evaluating the effectiveness of different network flow motifs in association football[J]. Journal of Quantitative Analysis in Sports, 2020, 16(4): 311-323.
[19] ÇOBANOGˇLU H O. Using of network motifs on basketball tactical analysis[J]. Journal of Education and Training Studies, 2019, 7(3S): 62-70.
[20] PICARD F, DAUDIN J J, KOSKAS M, et al. Assessing the exceptionality of network motifs[J]. Journal of Computational Biology, 2008, 15(1): 1-20.
[1] 高峰. 复杂网络深度重叠结构的发现[J]. 复杂系统与复杂性科学, 2024, 21(2): 15-21.
[2] 杨雅儒, 孙更新, 宾晟. 考虑多种预警信息的双层网络拥堵传播模型[J]. 复杂系统与复杂性科学, 2024, 21(2): 60-67.
[3] 邓中乙. 面向磨煤机组故障诊断的聚类粗化图模型[J]. 复杂系统与复杂性科学, 2024, 21(1): 152-158.
[4] 朱懋昌, 宾晟, 孙更新. 基于COVID-19传播特性的传染病模型的构建与研究[J]. 复杂系统与复杂性科学, 2023, 20(2): 29-37.
[5] 郭淑慧, 吕欣. 网络直播大数据:统计特征与时序规律挖掘[J]. 复杂系统与复杂性科学, 2023, 20(2): 1-9.
[6] 王佳亮, 李海滨, 李海燕. 基于复杂网络的新冠病毒群体免疫数值仿真[J]. 复杂系统与复杂性科学, 2023, 20(1): 27-33.
[7] 张书谙, 王曦, 代继鹏, 隋毅, 孙仁诚. 基于关键词共现网络的主题词提取算法[J]. 复杂系统与复杂性科学, 2023, 20(1): 74-80.
[8] 王一伊, 卜凡亮. 涉恐个体极端思想演化双阈值观点动力学模型[J]. 复杂系统与复杂性科学, 2022, 19(4): 55-63.
[9] 王浩, 许小可. 融合文本和表情符号特征的社交网络用户性别识别[J]. 复杂系统与复杂性科学, 2022, 19(4): 17-24.
[10] 赵薇, 李建波, 吕志强, 董传浩. 融合时间和地理信息的兴趣点推荐研究[J]. 复杂系统与复杂性科学, 2022, 19(4): 25-31.
[11] 李军涛, 胡启贤, 刘朋飞, 郭文文. 跨层穿梭车双提升机系统多目标问题优化[J]. 复杂系统与复杂性科学, 2022, 19(4): 80-90.
[12] 李冯, 宾晟, 孙更新. 基于时变参数的SCUIR传播模型的构建与研究[J]. 复杂系统与复杂性科学, 2022, 19(2): 80-86.
[13] 胡亮, 肖人彬, 王英聪. 蜂群激发抑制算法及其在交通信号配时中的应用[J]. 复杂系统与复杂性科学, 2019, 16(2): 9-18.
[14] 刘琪, 肖人彬. 观点动力学视角下基于意见领袖的网络舆情反转研究[J]. 复杂系统与复杂性科学, 2019, 16(1): 1-13.
[15] 李甍娜, 郭进利, 卞闻, 常宁戈, 肖潇, 陆睿敏. 网络视角下的唐诗[J]. 复杂系统与复杂性科学, 2017, 14(4): 66-71.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Baidu
map