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Hu, YQ; Ji, SG; Jin, YL; Feng, L3,4; Stanley, HE; Havlin, S7
Local structure can identify and quantify influential global spreaders in large scale social networks
Source PublicationPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Language英语
KeywordCOMPLEX NETWORKS EPIDEMICS VIRUSES
AbstractMeasuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node's global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.
2018
ISSN0027-8424
Volume115Issue:29Pages:7468-7472
Subject AreaScience & Technology - Other Topics
MOST Discipline CatalogueMultidisciplinary Sciences
DOI10.1073/pnas.1710547115
Indexed BySCIE
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Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/22861
Collection理论物理所科研产出_SCI期刊论文
Affiliation1.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Guangdong, Peoples R China
2.Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China
3.Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China
4.Agcy Sci Technol & Res, Comp Sci, Inst High Performance Comp, Singapore 138632, Singapore
5.Natl Univ Singapore, Dept Phys, Singapore 117551, Singapore
6.Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA
7.Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA
8.Bar Ilan Univ, Dept Phys, IL-52900 Ramat Gan, Israel
Recommended Citation
GB/T 7714
Hu, YQ,Ji, SG,Jin, YL,et al. Local structure can identify and quantify influential global spreaders in large scale social networks[J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2018,115(29):7468-7472.
APA Hu, YQ,Ji, SG,Jin, YL,Feng, L,Stanley, HE,&Havlin, S.(2018).Local structure can identify and quantify influential global spreaders in large scale social networks.PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,115(29),7468-7472.
MLA Hu, YQ,et al."Local structure can identify and quantify influential global spreaders in large scale social networks".PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 115.29(2018):7468-7472.
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