梁文,
朱凯丽
江西理工大学信息工程学院 赣州 341000
基金项目:国家自然科学基金(61662028)，江西省教育厅科学技术研究项目基金(GJJ170518)，江西省研究生创新专项资金项目(YC2018-S331)
详细信息
作者简介:杨书新：男，1979年生，副教授，研究方向为社会网络分析、生物信息学
梁文：男，1994年生，硕士生，研究方向为复杂网络、计算传播学
朱凯丽：女，1994年生，硕士生，研究方向为隐私保护、推荐系统
通讯作者:杨书新 yimuyunlang@sina.com
中图分类号:TP39计量
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被引次数:0
出版历程
收稿日期:2019-06-17
修回日期:2020-02-02
网络出版日期:2020-02-20
刊出日期:2020-06-04
Measurement of Node Influence Based on Three-level Neighbor in Complex Networks
Shuxin YANG,,Wen LIANG,
Kaili ZHU
Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Funds:The National Natural Science Foundation of China (61662028), The Scientific Technology Research Foundation of the Education Department of Jiangxi Province (GJJ170518), The Special Foundation of Postgraduate Innovation of Jiangxi province (YC2018-S331)
摘要
摘要:已有的节点影响力度量方法均存在一定的局限性。该文基于三度影响力原则，综合考虑局部度量的适宜层次及大规模网络的可扩展性，提出一种基于3级邻居的节点影响力度量方法(TIM)。该方法将节点2, 3级具有传播衰减特性的邻居视为整体，用于度量节点的影响能力。利用传染病模型及独立级联模型，在3个真实数据集验证了该方法的有效性。实验结果表明，基于3级邻居的节点影响力度量方法在影响力一致性、区分度、排序性等指标中表现优越，且能够有效求解影响力最大化问题。
关键词:复杂网络/
节点影响力/
影响力最大化
Abstract:There are some limitations in the existing metric methods for measuring node influence. A measurement method of node influence with three-level neighbors is proposed, which is based on the principle of three-degree influence, and considering the appropriate level of local measurement and the scalability of the large-scale network. Firstly, the neighbors with propagation attenuation characteristics in the second and third level of a node are regarded as a whole, which is used to measure the influence of the node. Then, an algorithm for measure called Three-level Influence Measurement (TIM) is proposed. Finally, in order to validate the effectiveness of the algorithm, the experiments on three datasets are conducted by using susceptible-infected-recovered model and independent cascade model. The experimental results show that the proposed algorithm is superior in consistency of influence, discrimination, sorting performance and other evaluation indexes. Furthermore, the TIM is applied to effectively solve the problem of maximizing influence.
Key words:Complex networks/
Influence of nodes/
Influence maximization
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