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Learning from Bees: An Approach for Influence Maximization on Viral Campaigns

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  • C Prem Sankar
  • Asharaf S.
  • K Satheesh Kumar

Abstract

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.

Suggested Citation

  • C Prem Sankar & Asharaf S. & K Satheesh Kumar, 2016. "Learning from Bees: An Approach for Influence Maximization on Viral Campaigns," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-15, December.
    • Handle: RePEc:plo:pone00:0168125
    DOI: 10.1371/journal.pone.0168125
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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Wu, Fang & Huberman, Bernardo A. & Adamic, Lada A. & Tyler, Joshua R., 2004. "Information flow in social groups," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 327-335.
    3. Yuqing Zhu & Weili Wu & Yuanjun Bi & Lidong Wu & Yiwei Jiang & Wen Xu, 2015. "Better approximation algorithms for influence maximization in online social networks," Journal of Combinatorial Optimization, Springer, vol. 30(1), pages 97-108, July.
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    Cited by:

    1. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.

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