关键词: 活跃性, 复合加权, 社团划分, 换手率, 成交量

Abstract: Currently, methods for community analysis can’t make full use of the intrinsic properties of complex systems, 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 dissimilarity 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 structures, 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 community 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.

Key words: activeness, composite weighted, community detection, turnover, volume