Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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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)
  • Received : 2013.05.23
  • Accepted : 2013.09.26
  • Published : 2014.09.30
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Abstract

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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References

  1. P. Domingos and M. Richardson, "Mining the network value of customers," in Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, August 26-29, 2001, pp. 57-66.
  2. M. Richardson and P. Domingos, "Mining knowledge-sharing sites for viral marketing," in Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, July 23-25, 2002, pp. 61-70.
  3. D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the spread of influence through a social network," in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, August 24-27, 2003, pp. 137-146.
  4. B. Liu, G. Cong, D. Xu, and Y. Zeng, "Time constrained influence maximization in social networks," in Proceedings of the IEEE 12th International Conference on Data Mining, Brussels, Belgium, December 10-13, 2012, pp. 439-448.
  5. W. Chen, C. Wang, and Y. Wang, "Scalable influence maximization for prevalent viral marketing in large-scale social networks," in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, July 25-28, 2010, pp. 1029-1038.
  6. W. Chen, Y. Wang, and S. Yang, "Efficient influence maximization in social networks," in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July 1, 2009, pp. 199-208.
  7. W. Chen, Y. Yuan, and L. Zhang, "Scalable influence maximization in social networks under the linear threshold model," in Proceedings of the IEEE 10th International Conference on Data Mining, Sydney, Australia, December 13-17, 2010, pp. 88-97.
  8. R. Narayanam and Y. Narahari, "A shapley value-based approach to discover influential nodes in social networks," IEEE Transactions on Automation Science and Engineering, vol. 8, no. 1, pp. 130-147, Jan. 2011. https://doi.org/10.1109/TASE.2010.2052042
  9. J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance, "Cost-effective outbreak detection in networks," in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, August 12-15, 2007, pp. 420-429.
  10. A. Goyal, W. Lu, and L. V. S. Lakshmanan, "CELF++: optimizing the greedy algorithm for influence maximization in social networks," in Proceedings of the 20th international conference companion on World wide web, Hyderabad, India, March 28-April 1, 2011, pp. 47-48.
  11. Y. Wang, G. Cong, G. Song, and K. Xie, "Community-based greedy algorithm for mining top-K influential nodes in mobile social networks," in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, July 25-28, 2010, pp. 1039-1048.
  12. J. Lv, J. Guo, and H. Ren, "A new community-based algorithm for influence maximization in social network," Journal of Computational Information Systems, vol. 9, no. 14, pp. 5659-5666, Jan. 2013.
  13. R. Tarjan, "Depth-first search and linear graph algorithms," SIAM Journal on Computing, vol. 1, no. 2, pp. 146-160, 1972. https://doi.org/10.1137/0201010
  14. A. Lancichinetti, S. Fortunato, and F. Radicchi, "Benchmark graphs for testing community detection algorithms," Physical Review E, vol. 78, no. 4, 046110, Oct. 2008. https://doi.org/10.1103/PhysRevE.78.046110

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