Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2669-2674.DOI: 10.11772/j.issn.1001-9081.2019020324

• Network and communications • Previous Articles     Next Articles

Important node identification method for dynamic networks based on H operation

SHAO Hao<sup>1</sup>, WANG Lunwen<sup>1</sup>, DENG Jian<sup>2</sup>   

  1. 1. Institute of Electronic Countermeasure, National University of Defense Technology, Hefei Anhui 230037, China;
    2. Second Department, Shijiazhuang Campus, Army Engineering University, Shijiazhuang Hebei 050003, China
  • Received:2019-02-27 Revised:2019-05-07 Online:2019-06-17 Published:2019-09-10
  • Supported by:

    This work is partially supported by Science and Technology Innovation Special Zone Project of Military (17-H863-01-ZT-003-204-03).


邵豪1, 王伦文1, 邓健2   

  1. 1. 国防科技大学 电子对抗学院, 合肥 230037;
    2. 陆军工程大学石家庄校区 二系, 石家庄 050003
  • 通讯作者: 邓健
  • 作者简介:邵豪(1995-),男,浙江金华人,硕士研究生,主要研究方向:网络拓扑识别、网络行为分析;王伦文(1966-),男,安徽怀宁人,教授,博士,主要研究方向:智能信息处理;邓健(1997-),男,安徽合肥人,主要研究方向:电子信息处理。
  • 基金资助:



Focused on the issue that the traditional important node identification method for K-shell networks needs global topology during iteration and cannot be used in dynamic networks, an important node identification method for dynamic networks based on neighborhood priority asynchronous H operation was proposed. Firstly, the algorithm was proved to converge to Ks (K-shell) value; then the degree of each node was taken as the initial value of h-index, and the nodes to be updated were selected by the h-index ranking of the node and the h-index change of the neighbor nodes; meanwhile the h-index was modified to adapt to the topology change according to the number change and maximum degree of the dynamic network nodes, finally the algorithm converged to the Ks and the important nodes were found. The simulation results show that the algorithm can find important nodes effectively by local information of neighbor nodes with less convergence time. Compared with the random selection algorithm and the neighborhood-variety selection algorithm, the convergence time of the proposed algorithm decreases by 77.4% and 28.3% respectively in static networks and 84.3% and 38.8% respectively in dynamic networks.

Key words: dynamic network, important node, h-index, H operation, K-shell, neighbor node



关键词: 动态网络, 重要节点, h指数, H运算, K-shell, 邻居节点

CLC Number: