Towards Efficient Influence Maximization for Evolving Social Networks

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)

Abstract

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21\(\times \) speedup in execution time while maintaining matching performance in terms of influence spread.

Keywords

Social Network Pruning Strategy Influence Maximization Influence Probability Influence Spread 
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.
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Notes

Acknowledgments

This research was supported by NSFC under grant NO. 61402511. The authors would like to thank the anonymous reviewers for their helpful comments.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiaodong Liu
    • 1
  • Xiangke Liao
    • 1
  • Shanshan Li
    • 1
  • Bin Lin
    • 1
  1. 1.National University of Defense TechnologyChangshaChina

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