logo

SCIENCE CHINA Information Sciences, Volume 60, Issue 10: 108101(2017) https://doi.org/10.1007/s11432-016-9080-3

Relative influence maximization in competitive social networks

More info
  • ReceivedDec 16, 2016
  • AcceptedApr 24, 2017
  • PublishedAug 9, 2017

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61472400, 61300105), Research Fund for Doctoral Program of Higher Education of China (Grant No. 2012351410010), Key Project of Science and Technology of Fujian (Grant No. 2013H6012), Key Laboratory of Network Data Science & Technology, and Chinese Science and Technology Foundation (Grant No. CASNDST20140X).


References

[1] Chen W, Collins A, Cummings R, et al. Influence maximization in social networks when negative opinions may emerge and propagate. In: Proceedings of 2012 SIAM International Conference on Data Mining, Mesa, 2011. 379--390. Google Scholar

[2] Budak C, Agrawal D, El Abbadi A. Limiting the spread of misinformation in social networks. In: Proceedings of the 20th International Conference on World Wide Web, Hyderabad, 2011. 665--674. Google Scholar

[3] Kempe D, Kleinberg J, Tardos É. 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, 2003. 137--146. Google Scholar

[4] Cheng S Q, Shen H W, Huang J M, et al. Staticgreedy: solving the scalability-accuracy dilemma in influence maximization. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, 2013. 509--518. Google Scholar

[5] Cheng S Q, Shen H W, Huang J M, et al. IMRank: influence maximization via finding self-consistent ranking. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, Gold Coast, 2014. 475--484. Google Scholar

[6] Chen W, Wang Y J, Yang S Y. Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 2009. 199--208. Google Scholar

  • Figure 1

    (Color online) Experimental results. (a) Relative influence of greedy algorithms with different $k$ in NetHEPT; (b) relative influence of greedy algorithms with different $k$ in Geom; (c) relative influence of heuristics with different $k$ in NetHEPT; (d) relative influence of heuristics with different $k$ in Geom; (e) running times with $k$ =100 in NetHEPT and Geom; (f) running times with $k$ =100 in synthetic networks.

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1