首页|基于邻居重叠比与结构洞的影响力最大化算法

基于邻居重叠比与结构洞的影响力最大化算法

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
影响力最大化是社交网络研究领域备受瞩目的问题之一,其目的是通过选择少量种子节点,尽量将影响力的传播范围最大化.传统的启发式算法往往只关注节点的单一特征,忽略了多个网络中心性指标的结合,受网络结构的影响较大,且容易导致"富人俱乐部"现象.为此,提出一种基于邻居重叠比与结构洞的影响力最大化算法OR-SH,通过邻居重叠比和结构洞性质两个指标衡量一个节点是否拥有成为种子节点的特征.在6个真实网络数据集中进行实验,发现该算法的影响传播范围相较基于节点覆盖范围与结构洞的NCSH算法平均提高了5.4%,表明ORSH算法能有效选取最有影响力的节点.
Influence Maximization Algorithm Based on Overlap Ratio of Neighbors and Structural Holes
Maximizing influence is one of the hot topics in the field of social network research,which aims to maximize the spread of influence by selecting a small number of seed nodes.Traditional heuristic algorithms often only focus on a single feature of a node,ignoring the combina-tion of multiple network centrality indicators,and are greatly influenced by network structure,which can easily lead to the phenomenon of"rich club".To this end,a maximum influence algorithm ORSH based on neighbor overlap ratio and structural holes is proposed,which mea-sures whether a node has the characteristics to become a seed node through two indicators:neighbor overlap ratio and structural hole proper-ties.Experiments were conducted on six real network datasets,and it was found that the influence propagation range of this algorithm was in-creased by an average of 5.4%compared to the NCSH algorithm based on node coverage and structural holes,indicating that the ORSH algo-rithm can effectively select the most influential nodes.

social networkinfluence maximizationoverlap ratio of neighborsstructural holeheuristic algorithm

洪泽坚、莫欣岳、李欢

展开 >

海南大学 网络空间安全学院(密码学院),海南 海口 570228

社交网络 影响力最大化 邻居重叠比 结构洞 启发式算法

教育部产学合作协同育人项目中国高等教育学会高等教育科学研究规划课题海南省自然科学基金项目海南省自然科学基金项目海南大学科研启动基金项目海南大学教育教学改革研究项目

22090207016253822LH0409623RC455623RC457KYQDZR-22097hdjy2364

2024

10.11907/rjdk.231702

软件导刊
湖北省信息学会

软件导刊

影响因子：0.524
ISSN：1672-7800
年,卷(期)：2024.23(7)