Stock Network Community Detection Method Based on Influence Calculating Model

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.