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Multimedia Tools and Applications

, Volume 75, Issue 15, pp 8829–8841 | Cite as

Effectively identifying the influential spreaders in large-scale social networks

  • Yingjie Xia
  • Xiaolong Ren
  • Zhengchao Peng
  • Jianlin Zhang
  • Li She
Article

Abstract

With great theoretical and practical significance, the studies of information spreading on social media become one of the most exciting domains in many branches of sciences. How to control the spreading process is of particular interests, where the identification of the most influential nodes in larger-scale social networks is a crucial issue. Degree centrality is one of the simplest method which supposes that the node with more neighbours may be more influential. K-shell decomposition method partitions the networks into several shells based on the assumption that nodes in the same shell have similar influence and nodes in higher-level shells (e.g., central) are probably to infect more nodes. Degree centrality and k-shell decomposition are local methods which are efficient but less relevant. Global methods such as closeness and betweenness centralities are more exact but time-consuming. For effectively identifying the more influential spreaders in large-scale social networks, in this paper we proposed an algorithm framework to solve this dilemma by combining the local and global methods. All the nodes are graded by the local methods and then the periphery of the network is removed according to their central values. At last, the global methods are employed to find out which node is more influential. The experimental results show that our framework can be efficient and even more accurate than the global methods

Keywords

Important nodes Node centrality Node ranking Large-scale networks 

Notes

Acknowledgements

This paper draws on work supported in part by the following funds: National Natural Science Foundation of China under grant number 61472113, 61304188 and 11205042, Zhejiang Provincial Natural Science Foundation of China under grant number LZ13F020004, LR14F020003 and Y6110317, and CCF-Tencent Open Research Fund.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Yingjie Xia
    • 1
  • Xiaolong Ren
    • 2
  • Zhengchao Peng
    • 3
  • Jianlin Zhang
    • 2
  • Li She
    • 2
  1. 1.Intelligent Transportation and Information Security LabHangzhou Normal UniversityZhejiangChina
  2. 2.Alibaba Research Center for Complexity Sciences, Alibaba Business CollegeHangzhou Normal UniversityZhejiangChina
  3. 3.Web Sciences CenterUniversity of Electronic Science and Technology of ChinaSichuanChina

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