Big social network influence maximization via recursively estimating influence spread
文献类型：期刊论文
| 作者 | Lu, Wei-Xue1 ; Zhou, Chuan2,4; Wu, Jia3
|
| 刊名 | KNOWLEDGE-BASED SYSTEMS
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000387519500013 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/24084]  |
| 专题 | 应用数学研究所 |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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