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复杂系统与复杂性科学  2014, Vol. 11 Issue (4): 48-53    DOI: 10.13306/j.1672-3813.2014.04.009
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基于同步理论的股票网络社团识别研究
麻景豪, 蔡世民
电子科技大学计算机科学与工程学院,成都 611731
Study on Community Identification of Stock Network Based on Synchronization Theory
MA Jinghao, CAI Shimin
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731
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摘要 利用股票价格波动时间序列的相关特性,基于同步理论研究股票网络的社团结构。通过对关联矩阵的谱分析确定股票网络中存在复杂的社团结构。随后,利用基于Kuramoto模型的同步聚类算法对网络节点(股票)进行动态分组,由局部序参量确定算法的收敛性并得到稳定的社团结构。通过与快速社团检测算法的对比验证,表明基于Kuramoto模型的同步聚类算法能够正确得到股票网络的社团结构,且更符合股票的属性分类。
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麻景豪
蔡世民
关键词 同步理论股票网络社团结构社团检测    
Abstract:The substructure of stock network, e.g. community, is investigated based on the synchronization theory by utilizing the correlation matrix of time series of stock price fluctuation. Through the spectral analysis on the correlation matrix, it’s determined that the complicated community structure obviously exists in the stock network. Then, the clustering algorithm based on the synchronization theory incorporating with the Kuramoto model is used to dynamically identify the community structure, which suggests that the groups of stocks well agree with the taxonomy of stock market. We also apply the fast community detecting algorithm to verify the former results with respect to the same parametric constrains.
Key wordssynchronization theory    stock network    community structure    community detection
收稿日期: 2013-07-31      出版日期: 2026-06-22
基金资助:国家自然科学基金(61004102);中央高校基本科研业务费专项基金(ZYGX2012J075)
通讯作者: 蔡世民(1981-),男,江苏苏州人,博士,副教授,主要研究方向为复杂系统和复杂网络理论及应用,大规模数据挖掘和时间序列分析。   
作者简介: 麻景豪(1990-),男,河南南阳人,硕士研究生,主要研究方向为复杂网络理论应用于大规模数据挖掘。
引用本文:   
麻景豪, 蔡世民. 基于同步理论的股票网络社团识别研究[J]. 复杂系统与复杂性科学, 2014, 11(4): 48-53.
MA Jinghao, CAI Shimin. Study on Community Identification of Stock Network Based on Synchronization Theory[J]. Complex Systems and Complexity Science, 2014, 11(4): 48-53.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2014.04.009      或      https://fzkx.qdu.edu.cn/CN/Y2014/V11/I4/48
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