Netinfo Security ›› 2024, Vol. 24 ›› Issue (11): 1665-1674.doi: 10.3969/j.issn.1671-1122.2024.11.006

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Target Personnel Importance Ranking Algorithm Based on Improved Weighted LeaderRank

XIA Lingling1,2, MA Zhuo1,2(), GUO Xiangmin1,2, NI Xueli1,2   

  1. 1. Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing 210031, China
    2. Jiangsu Electronic Data Forensics and Analysis Engineering Research Center, Nanjing 210031, China
  • Received:2024-08-06 Online:2024-11-10 Published:2024-11-21

Abstract:

At present, the manual analysis of complex interpersonal relationship data is faced with challenges, especially the problems of insufficient accuracy, low efficiency and high cost for the importance assessment of important individuals. To solve this problem, this paper comprehensively considered behavioral characteristics and activity rules of this type of personnel, based on call detail records of key personnel and the weighted LeaderRank algorithm, and assigned weight to multiple factors such as call duration, call frequency, night call frequency and the number of key individuals among contacts. As a result, it proposed an improved weighted LeaderRank algorithm to rank the importance of key contacts and screen out target people with similar behavior patterns and activity characteristics as important individuals. The experimental results show that compared with classical influence node discovery algorithms such as the degree centrality algorithm, the closeness centrality algorithm and the betweenness centrality algorithm, the improved weighted LeaderRank algorithm has a higher score for target people with similar behavior characteristics in the communication relationship, and can effectively identify potential and unobserved target people in the communication relationship.

Key words: LeaderRank, complex network analysis, node importance ranking, association mining

CLC Number: