HHa中心性算法:一种基于h指数和Ha指数的复杂网络节点排序算法
HHa Centrality Algorithm: A Node Centrality Algorithm Based on the H-Index and Ha-Index
[目的/意义] 针对复杂网络中的重要节点的识别，设计一种节点中心性算法，在传染病防控、舆情监控、产品营销、人才发现等方面发挥作用。[方法/过程] 同时考虑节点的高影响力邻居的数量及其总体影响，提出HHa节点中心性算法，在真实网络和人工网络上，使用SIR传染病模型模拟信息传播过程，采用单调函数M和肯德尔相关系数作为评价指标验证HHa中心性算法的有效性、准确性以及稳定性。[结果/结论] 实验表明，与7种经典的中心性算法相比，HHa中心性算法得出的排序结果M值为0.999等，排名第2；肯德尔系数为0.845等，高于其他算法0.15左右，排名第1且表现稳定。采用HHa中心性算法识别网络中的重要节点具备可行性。
计算技术、计算机技术
复杂网络节点中心性节点影响力h指数HHa中心性算法
岳名亮,马廷灿,刘佳程.HHa中心性算法:一种基于h指数和Ha指数的复杂网络节点排序算法[EB/OL].(2023-04-01)[2025-09-26].https://chinaxiv.org/abs/202304.00460.点此复制
[Purpose/significance] For the identification of important nodes in complex networks, the paper designs a node centrality algorithm, which plays an important role in infectious disease prevention and control, public opinion monitoring, product marketing, talent discovery and so on. [Method/process] This paper proposed a new node centrality algorithm, the HHa node centrality algorithm, taking both the number of the node's high influence neighbors and their total influence into consideration. On the real network and artificial network, the Susceptible-Infected-Recovered (SIR) model was used to simulate the information dissemination process, and the monotonic function M and Kendall correlation coefficient were used as evaluation indicators to verify the effectiveness, accuracy and stability of the HHa centrality algorithm. [Result/conclusion] The experimental results show that, compared with the 7 classic centrality algorithms, the HHa centrality algorithm ranks 2nd with a monotonic result of 0.999, and the Kendall coefficient is 0.845, which is higher than other algorithms' accuracy about 0.15, ranking 1st and performing robustly. It is feasible to use HHa centrality algorithm to identify important nodes in the network.
complex networksnode centralitynode influenceh-indexHHa centrality algorithm
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