Journal of Information Processing Systems

  • 제10권3호
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  • Pages.471-482
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  • 2014
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Efficient Greedy Algorithms for Influence Maximization in Social Networks

  • Lv, Jiaguo (School of Information Science and Engineering, Yanshan University) ;
  • Guo, Jingfeng (School of Information Science and Engineering, Yanshan University) ;
  • Ren, Huixiao (School of Information Science and Engineering, Yanshan University)
  • 투고 : 2013.05.23
  • 심사 : 2013.09.26
  • 발행 : 2014.09.30
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초록

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.

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참고문헌

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