基于局部近邻节点H指数的复杂网络节点重要性排序算法  

Algorithm for Ranking the Importance of Nodes in Complex Networks Based on the H-index of Local Nearest Neighbors

作　　者：董香凝 宾晟 孙更新 DONG Xiang-ning;BIN Sheng;SUN Geng-xin(School of Computer Science and Technology,Qingdao University,Qingdao 266071,China)

机构地区：[1]青岛大学计算机科学技术学院,青岛266071

出　　处：《青岛大学学报（自然科学版）》2023年第3期34-40,49,共8页Journal of Qingdao University(Natural Science Edition)

基　　金：教育部人文社会科学研究青年项目(批准号:15YJC860001)资助;山东省自然科学基金面上项目(批准号:ZR2017MG011)资助;山东省社会科学规划项目(批准号:17CHLJ16)资助。

摘　　要：为了更高效地检测排名复杂网络中节点的重要性,提出一种局部近邻节点H指数的节点重要性排序算法,引入近邻节点的次要影响力比重系数来衡量邻居节点对节点本身发挥的影响力比重。在真实网络中,使用易感者—感染者—恢复者(Susceptible-Infected-Recovered,SIR)传播模型模拟信息传播过程,选取kendall相关系数、互补累积分布函数和单调函数作为性能评价指标验证该算法的有效性。实验结果证明,局部近邻节点H指数算法既能够有效评估不同网络中有影响力的节点,又具有很好的区分能力。A node importance ranking algorithm with H-index of local nearest neighbor node was proposed in order to detect the importance of nodes in ranking complex network more efficiently,and the secondary influence weight coefficient of the nearest neighbor nodes was introduced to measure the weight of influence exerted by neighbor nodes on the nodes themselves.In the real complex network,the Susceptible-Infected-Recovered(SIR)propagation model was used to simulate the information propagation process,and Kendall correlation coefficient,complementary cumulative distribution function and monotone function were selected as performance evaluation indexes to test the effectiveness of the algorithm.The experimental results show that the local nearest neighbor node H-index algorithm is both effective in evaluating the influential nodes in different networks and has good distinguishing ability.

关 键 词：复杂网络 H指数 SIR模型 邻居节点 节点重要性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]