Skip to main content
Log in

Solution of Bharathi–Kempe–Salek conjecture for influence maximization on arborescence

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

Abstract

Influence maximization is an important problem in social networks. Bharathi et al. (Competitive influence maximization in social networks, pp 306–311, 2007) conjectured that this problem is \({{\mathcal {N}}}{{\mathcal {P}}}\)-hard on arborescence directed into a root. In this short note, we show that the conjecture is true for the independent cascade (IC) model, which is the most studied model in the literature specifying how each node influences other nodes. Therefore, assuming \({\mathcal {P}}\ne {{\mathcal {N}}}{{\mathcal {P}}}\), there exists no polynomial-time algorithm for the influence maximization problem under the IC model on arborescence directed into a root. On the other hand, Wang et al. (J Comb Optim, doi:10.1007/s10878-016-9991-1, 2016) have shown that there exists polynomial-time algorithm for this problem under the linear threshold (LT) model. Hence, this pair of results is of theoretical interest since this is the first time to give a theoretical difference on computational complexity between the IC and LT models. We believe it may motivate further research for influence maximization on arborescence and other special graphs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Bharathi S, Kempe D, Salek M (2007) Competitive influence maximization in social networks. In: WINE, pp 306–311

  • Brown J, Reinegen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14:350–362

    Article  Google Scholar 

  • Chen N (2008) On the approximability of influence in social networks. In: The 2008 annual ACM SIAM symposium on Discrete algorithms, pp 1029–1037

  • Chen W, Wang C, Wang Y (2010) Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD

  • Chen W, Yuan Y, Zhang L (2010) Scalable influence maximization in social networks under the linear threshold model. In: ICDM

  • Domingos P, Richardson M (2001) Mining the network value of customers. In: KDD, pp 57–66

  • Fan L, Lu Z, Wu W, Bi Y, Wang A, Thuraisingham B (2014) An individual-based model of information diffusion combining friends’ influence. J Comb Optim 28(3):529–539

    Article  MathSciNet  MATH  Google Scholar 

  • Goldenberg J, Libai B, Muller E (2001a) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12:211–223

  • Goldenberg J, Libai B, Muller E (2001b) Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad Mark Sci Rev 2001:1

  • Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443

    Article  Google Scholar 

  • He X, Kempe D (2014) Stability of influence maximization. In: KDD, pp 1256–1265

  • Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of infuence through a social network. In: KDD, pp 137–146

  • Kempe D, Kleinberg J, Tardos E (2005) Influential nodes in a diffusion model for social networks. In: International colloquium on automata, languages and programming, pp 1127–1138

  • Lu Z, Zhang W, Wu W, Fu B, Du D-Z (2011) Approximation and inapproximation for the influence maximization problem in social networks under deterministic linear threshold model. In: ICDCS Workshops, pp 160–165

  • Richardson M, Domingos V (2002) Mining knowledge-sharing sites for viral marketing. In: KDD, pp 61–70

  • Schelling T (1978) Micromotives and macrobehavior. Norton, New York

    Google Scholar 

  • Tong G, Wu W, Tang S, Du D-Z (2015) Adaptive influence maximization in dynamic social networks. CoRR. arXiv:1506.06294

  • Wang A, Wu W, Cui L (2016) On Bharathi-Kempe-Salek conjecture for influence maximization on arborescence. J Comb Optim. doi:10.1007/s10878-016-9991-1

  • Xu Wen Lu, Weili Zaixin, Wu, Zhiming Chen (2014) A novel approach to online social influence maximization. Soc Netw Anal Min 4(1):153

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by National Natural Science Foundation of China under (61472272, 11531011), and Xinjiang Talent Youth Project (2013711011).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Zaixin Lu or Zhao Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Z., Zhang, Z. & Wu, W. Solution of Bharathi–Kempe–Salek conjecture for influence maximization on arborescence. J Comb Optim 33, 803–808 (2017). https://doi.org/10.1007/s10878-016-0006-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-016-0006-z

Keywords

Navigation