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Efficient Influence Maximization in Weighted Independent Cascade Model

  • Yaxuan WangEmail author
  • Hongzhi Wang
  • Jianzhong Li
  • Hong Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)

Abstract

Influence maximization (IM) problem which aims to find the most influential seed set in a social network plays an important role in viral marketing. However, previous solutions pay all attention to the structure of network, which causes trouble in real-word applications.

D. Kempe et al. [8] presented that a non-negative weight can be attached to each node to extend the applicability of traditional models. Although this idea is much applicable in practice, there is little research based on this opinion. Thus, we develop substantial study about this issue. We extend the Independent Cascade (IC) model and present Weighted IC (WIC) model. The IM problem in WIC model is NP-hard. To solve this problem, we present a basic greedy algorithm and Weight Reset (WR) algorithm. Moreover, we propose Bounded WR (BWR) algorithm, a Fully Polynomial-Time Approximation Scheme (FPTAS).

Experimentally, WIC model outperforms IC model in nearly \(90\,\%\) in weighted IM problem. Moreover, BWR achieves excellent approximation and efficiency which is faster than greedy algorithm more than four orders of magnitude. Especially, BWR can handle huge networks with millions of nodes in several tens of seconds while keeping high accuracy. This result demonstrates the effectiveness and efficiency of BWR.

Keywords

Greedy Algorithm Node Selection Giant Component Influence Maximization Independent Cascade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This paper was supported by NGFR 973 grant 2012CB316200, NSFC grant U1509216,61472099,61133002 and National Sci-Tech Support Plan 2015BAH10F01.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yaxuan Wang
    • 1
    Email author
  • Hongzhi Wang
    • 1
  • Jianzhong Li
    • 1
  • Hong Gao
    • 1
  1. 1.Harbin Institute of TechnologyHarbinChina

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