Influence Maximization Algorithm Based on Overlapping Community

被引：0
|
作者：
Qiu L. [1 ]
Jia W. [1 ]
Fan X. [1 ]
机构：
[1] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao
来源：
Data Analysis and Knowledge Discovery | 2019年 / 3卷 / 07期
关键词：
Influence Maximization; Overlapping Community; Social Network;
D O I：
10.11925/infotech.2096-3467.2018.1389
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
[Objective] This paper proposes a new algorithm for influence maximization based on overlapping community, called IM-BOC algorithm, aiming to the low efficiency of greedy algorithm. [Methods] This method selects candidate seed set by combing propagation degree and k-core firstly, then it utilizes CELF algorithm to ensure the optimal seed set, which can improve both efficiency and accuracy. [Results] The experimental results show that running time of our algorithm can improve about 89% when facing Amazon dataset. [Limitations] Our IM-BOC algorithm allocates the number of candidate seeds only according to the number of community nodes, which has insufficient theoretical evidence. [Conclusions] IM-BOC algorithm is applicable to large scale networks under the premise of ensuring the influence spread. © 2019 The Author(s).
引用
收藏
页码：94 / 102
页数：8
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