基于投票机制的社交网络影响力节点集识别算法  

Identification of a Set of Influential Nodes in Social Networks Based on Voting Mechanism

作　　者：赵欢 徐桂琼 Zhao Huan;Xu Guiqiong(Department of Information Management,School of Management,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学管理学院信息管理系,上海200444

出　　处：《数据分析与知识发现》2024年第6期107-118,共12页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:23BGL270)的研究成果之一。

摘　　要：【目的】为了降低社交网络中种子节点之间的影响重叠程度,提出基于投票机制的社交网络影响力节点集识别算法KSEVoteRank。【方法】综合考虑节点重要性和邻域信息,定义节点投票能力,设计投票分配策略,同时引入衰减因子折扣邻居的投票能力,最后基于投票得分迭代选出高影响力节点。【结果】实验结果表明,在大型社交网络Ca-AstroPh数据集中KSEVoteRank算法选出的影响力节点集的影响重叠程度比VoteRank算法降低约21%。【局限】在重复投票过程中,设置邻居的投票分配策略不变,可能导致一些误差。【结论】基于投票机制的KSEVoteRank算法能够分散性选取高影响力节点,实现较大范围的影响传播。[Objective]This paper aims to achieve a trade-off between running efficiency and accuracy,this paper proposes a voting-based algorithm for identifying a set of influential nodes in social networks named KSEVoteRank.[Methods]Considering the node importance and the neighborhood information,the voting ability of a node is defined and a voting allocation strategy is designed.Meanwhile,an attenuation factor is introduced to discount the voting ability of neighbors.Finally,the node with the highest voting score is iteratively selected as the seed node.[Results]The experimental results show that the influence overlap of a set of influential nodes detected by the KSEVoteRank algorithm in the large social network Ca-AstroPh dataset is about 21%less than that of the VoteRank algorithm.[Limitations]During the repeated voting process,the voting allocation strategy of the neighbors is fixed,which may cause a slight deviation in the theoretical results.[Conclusions]The KSEVoteRank algorithm,based on a voting mechanism,selects a set of influential nodes in a distributed manner to achieve a widespread propagation of influence,which is applicable to large social networks.

关 键 词：社交网络 影响最大化 投票机制 衰减因子 

分 类 号：TP301[自动化与计算机技术—计算机系统结构] O157[自动化与计算机技术—计算机科学与技术]