ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于影响力计算模型的股票网络社团划分方法

1. (合肥工业大学计算机与信息学院 合肥 230009) (jsjxwangh@hfut.edu.cn)
• 出版日期: 2014-10-01
• 基金资助:
国家“九七三”重点基础研究发展计划基金项目(2011CB302501)；国家自然科学基金项目(61332009)；计算机体系结构国家重点实验室开放课题(CARCH201203)；北京市教委科技计划面上项目(KM201210028004)

### Stock Network Community Detection Method Based on Influence Calculating Model

Wang Hao, Li Guohuan, Yao Hongliang, Li Junzhao

1. (School of Computer and Information, Hefei University of Technology, Hefei 230009)
• Online: 2014-10-01

Abstract: Taking advantage of the energy characteristics of complex system, a concept of influence is introduced to research community detection method, so that community structure could be discovered effectively. With regard to the stock closing price, by introducing the definition of influence and node centrality, a stock network is construted with influence which is regarded as the edge weight. This paper proposes an algorithm named stock network hierarchical clustering based on the influence calculating model, which is referred to as BCNHC algorithm. Firstly, BCNHC algorithm introduces the definition of nodes’ activity and influence, and puts forward the influence calculating model of node in networks in addition. Then, on the basis of measure criterion of the node centrality, the nodes with large node centrality value as the center nodes are selected, and the nodes’ Intimacy and influence model are utilized to ensure the influence of association between neighbor nodes. Furthermore, the node with minimum degree is gathering toward to center nodes, so as to reduce the error clustering caused by the uncertainty of which community neighbor nodes belong to. On the basis, the neighbor communities are clustered with the average influence of association of communities. It guarantees that influence of association reach to maximization for all the nodes in the community, until the entire networks’ modularity come to maximum. At last, comparison and analysis of experimental on stock network prove the feasibility of BCNHC algorithm.