Big social network influence maximization via recursively estimating influence spread

doi:10.1016/j.knosys.2016.09.020

	Big social network influence maximization via recursively estimating influence spread
	Lu, Wei-Xue 1; Zhou, Chuan 2,4; Wu, Jia 3
刊名	KNOWLEDGE-BASED SYSTEMS
	2016-12-01
卷号	113 页码:143-154
关键词	Greedy algorithms Social network Influence maximization
ISSN号	0950-7051
DOI	10.1016/j.knosys.2016.09.020
英文摘要	Influence maximization aims to find a set of highly influential nodes in a social network to maximize the spread of influence. Although the problem has been widely studied, it is still challenging to design algorithms to meet three requirements simultaneously, i.e., fast computation, guaranteed accuracy, and low memory consumption that scales well to a big network. Existing heuristic algorithms are scalable but suffer from unguaranteed accuracy. Greedy algorithms such as CELF [1] are accurate with theoretical guarantee but incur heavy simulation cost in calculating the influence spread. Moreover, static greedy algorithms are accurate and sufficiently fast, but they suffer extensive memory cost. In this paper, we present a new algorithm to enable greedy algorithms to perform well in big social network influence maximization. Our algorithm recursively estimates the influence spread using reachable probabilities from node to node. We provide three strategies that integrate memory cost and computing efficiency. Experiments demonstrate the high accuracy of our influence estimation. The proposed algorithm is more than 500 times faster than the CELF algorithm on four real world data sets. (C) 2016 Elsevier B.V. All rights reserved.
资助项目	973 Program[2013CB329605] ; NSFC[61502479] ; NSFC[61372191] ; NSFC[61572492] ; Strategic Leading Science and Technology Projects of CAS[XDA06030200] ; Australian Research Council (ARC) Discovery Projects[DP140100545] ; China Scholarship Council Foundation[201206410056]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000387519500013
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/24084]
专题	中国科学院数学与系统科学研究院
通讯作者	Wu, Jia
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China 3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst QCIS, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Lu, Wei-Xue,Zhou, Chuan,Wu, Jia. Big social network influence maximization via recursively estimating influence spread[J]. KNOWLEDGE-BASED SYSTEMS,2016,113:143-154.
APA	Lu, Wei-Xue,Zhou, Chuan,&Wu, Jia.(2016).Big social network influence maximization via recursively estimating influence spread.KNOWLEDGE-BASED SYSTEMS,113,143-154.
MLA	Lu, Wei-Xue,et al."Big social network influence maximization via recursively estimating influence spread".KNOWLEDGE-BASED SYSTEMS 113(2016):143-154.