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A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks | |
Tang, Jianxin1,2; Zhang, Ruisheng1; Wang, Ping1; Zhao, Zhili1; Fan, Li1; Liu, Xin1 | |
2020-01 | |
发表期刊 | KNOWLEDGE-BASED SYSTEMS |
ISSN | 0950-7051 |
卷号 | 187 |
摘要 | Influence maximization problem aims to select a subset of k most influential nodes from a given network such that the spread of influence triggered by the seed set will be maximum. Greedy based algorithm sare time-consuming to approximate the expected influence spread o f given node set accurately and not well scalable to large-scale networks especially when the propagation probability is large. Conventional heuristics based on network topology or confined diffusion paths tend to suffer from the problem of low solution accuracy or huge memory cost. In this paper an effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way. Novel encoding mechanism and discrete evolutionary rules are conceived based on network topology structure for virtual frog population. To facilitate the global exploratory solution, a novel local exploitation mechanism combining deterministic and random walk strategies is put forward to improve the suboptimal meme of each memeplex in the frog population. The experimental results of influence spread in six real-world networks and statistical tests show that DSFLA perform s effectively in selecting targeted influential seed nodes for influence maximization and is superior than several state-of-the-art alternatives. (C) 2019 Elsevier B.V. All rights reserved. |
关键词 | Social networks Viral marketing Influence maximization Discrete shuffled frog-leaping algorithm Swarm intelligence |
DOI | 10.1016/j.knosys.2019.07.004 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundations of China[21503101][61702240] ; CERNET Innovation Project, China[NGII20170422] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000501653900027 |
出版者 | ELSEVIER |
EI入藏号 | 20192907196035 |
EI主题词 | Economic and social effects |
EI分类号 | 723 Computer Software, Data Handling and Applications - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 971 Social Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/76621 |
专题 | 计算机与通信学院 |
通讯作者 | Zhang, Ruisheng |
作者单位 | 1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China; 2.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China |
第一作者单位 | 兰州理工大学 |
推荐引用方式 GB/T 7714 | Tang, Jianxin,Zhang, Ruisheng,Wang, Ping,et al. A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks[J]. KNOWLEDGE-BASED SYSTEMS,2020,187. |
APA | Tang, Jianxin,Zhang, Ruisheng,Wang, Ping,Zhao, Zhili,Fan, Li,&Liu, Xin.(2020).A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks.KNOWLEDGE-BASED SYSTEMS,187. |
MLA | Tang, Jianxin,et al."A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks".KNOWLEDGE-BASED SYSTEMS 187(2020). |
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