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Parallel Seed Selection for Influence Maximization Based on k-shell Decomposition

  • Hong Wu
  • Kun YueEmail author
  • Xiaodong Fu
  • Yujie Wang
  • Weiyi Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Influence maximization is the problem of selecting a set of seeds in a social network to maximize the influence under certain diffusion model. Prior solutions, the greedy and its improvements are time-consuming. In this paper, we propose candidate shells influence maximization (CSIM) algorithm under heat diffusion model to select seeds in parallel. We employ CSIM algorithm (a modified algorithm of greedy) to coarsely estimate the influence spread to avoid massive estimation of heat diffusion process, thus can effectively improve the speed of selecting seeds. Moreover, we can select seeds from candidate shells in parallel. Specifically, First, we employ the k-shell decomposition method to divide a social network and generate the candidate shells. Further, we use the heat diffusion model to model the influence spread. Finally, we select seeds of candidate shells in parallel by using the CSIM algorithm. Experimental results show the effectiveness and feasibility of the proposed algorithm.

Keywords

Parallel Social networks Influence maximization K-shell decomposition 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 61472345, 61462056, 61402398), Natural Science Foundation of Yunnan Province (Nos. 2014FA023, 2014FA028), Program for Excellent Young Talents of Yunnan University (No. XT412003), Research Foundation of the Education Department of Yunnan Province (Nos. 2014C134Y, 2016YJS005, 2016ZZX013).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Hong Wu
    • 1
    • 2
  • Kun Yue
    • 1
    Email author
  • Xiaodong Fu
    • 3
  • Yujie Wang
    • 1
  • Weiyi Liu
    • 1
  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingChina
  2. 2.School of Information EngineeringQujing Normal UniversityQujingChina
  3. 3.Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina

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