CAJ | 학술논문

当前社团分析方法没有充分利用复杂系统的内在特性，难以准确和有效地发现复杂加权网络群体之间的相关性。基于股票网络的活跃性，提出了一种基于活跃性的复合加权股票网络的层次社团划分算法。该算法对股票活跃性进行了定义，提出了一种复合加权模型以有效表示股票网络的活跃性，进而为了实现复合加权网络的社团划分，给出了群体相异度的评判标准。该算法以股票价格波动的相关性为边建立复合加权股票网络，以股票的换手率和成交量为评价标准，选出活跃性高的股票，进而以活跃性股票为中心，基于股票间的相异度权重标价准则，提取多个高活跃的局部结构，可以有效避免层次划分算法由于初始社团结构质量不高，导致社区结构不能沿正确方向继续进行层次发现的问题。最后，基于高活跃的局部结构性，利用全局优化模块度的方法对复合加权网络进行社团划分。将CNM算法（Newman贪婪算法）与BGLL算法运用于构建的网络中，结果表明了算法的优越性。
당전사단분석방법몰유충분이용복잡계통적내재특성，난이준학화유효지발현복잡가권망락군체지간적상관성。기우고표망락적활약성，제출료일충기우활약성적복합가권고표망락적층차사단화분산법。해산법대고표활약성진행료정의，제출료일충복합가권모형이유효표시고표망락적활약성，진이위료실현복합가권망락적사단화분，급출료군체상이도적평판표준。해산법이고표개격파동적상관성위변건립복합가권고표망락，이고표적환수솔화성교량위평개표준，선출활약성고적고표，진이이활약성고표위중심，기우고표간적상이도권중표개준칙，제취다개고활약적국부결구，가이유효피면층차화분산법유우초시사단결구질량불고，도치사구결구불능연정학방향계속진행층차발현적문제。최후，기우고활약적국부결구성，이용전국우화모괴도적방법대복합가권망락진행사단화분。장CNM산법（Newman탐람산법）여BGLL산법운용우구건적망락중，결과표명료산법적우월성。
Currently, methods for community analysis can’t make full use of the intrinsic properties of complex sys-tems, so it is difficult to find a correlation among groups in complex weighted network accurately and effectively. Based on the active property of stock network, this paper proposes a hierarchical community detection algorithm for composite weighted stock network. This algorithm makes the definition on stock activity, puts forward a composite weighted model to effectively represent the activity of stock network, and presents the criteria to evaluate the dissim-ilarity among groups in order to achieve the community detection for composite weighted network. This algorithm constructs a composite weighted stock network on correlation for the side of the stock price volatility. Taking the turnover and volume of stock as evaluation standard, it chooses high-activeness stocks. Furthermore, centered on the chosen stocks, based on stock inter-dissimilarity weight price guidelines, it extracts multiple high active local struc-tures, thus effectively avoids the matter that the community can’t be discovered in right direction because of the low quality initial community structure. Finally, based on the high active local structures, this paper divides the commu-nity in weighted network by globally optimizating the modularity. The CNM algorithm and BGLL algorithm are applied to the established network, the comparison results show the superiority of the algorithm.