雅卡索引
中心性
计算机科学
数据挖掘
节点(物理)
熵(时间箭头)
钥匙(锁)
数学
人工智能
聚类分析
物理
统计
计算机安全
量子力学
作者
Lidong Fu,Xin Ma,Zengfa Dou,Yun Bai,Xi Zhao
出处
期刊:Entropy
[Multidisciplinary Digital Publishing Institute]
日期:2024-11-30
卷期号:26 (12): 1041-1041
摘要
In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.
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