Skip to main content
SpringerLink
Log in

Probability-Based Multi-hop Diffusion Method for Influence Maximization in Social Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Influence maximization is the problem of finding a subset of nodes that maximizes the spread of information in a social network. Many solutions have been developed, including greedy and heuristics based algorithms. While the former is very time consuming that might be impractical in many cases, the later is feasible in terms of computational time, but its influence spread is not guaranteed because of limitations in the algorithm. In this study, we propose a new heuristic algorithm which considers the propagation probabilities of nodes in the network individually and takes into account the effect of multi-hop neighbors, thus, it can achieve higher influence spread. A realistic network model with non-uniform propagation probabilities between nodes is assumed in our algorithm. We also examine the optimal number of hops of neighbors to be considered in the algorithm. Experiments using real-world social networks showed that our proposed method outperformed the previous heuristic-based approaches.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www-personal.umich.edu/~mejn/netdata/.

References

  1. Domingos, P., & Richardson, M. (2001). Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 57–66). ACM.

  2. Richardson, M., & Domingos, P. (2002). Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 61–70). ACM.

  3. Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 137–146). ACM.

  4. Leskovec. J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., & Glance, N. (2007). Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 420–429). ACM.

  5. Goyal, A., Lu, W., & Lakshmanan, L.V. (2011). Celf++: optimizing the greedy algorithm for influence maximization in social networks. In Proceedings of the 20th international conference companion on World wide web (pp. 47–48). ACM.

  6. Chen, W., Wang, Y., & Yang, S. (2009). Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 199–208). ACM.

  7. Kimura, M., & Saito, K. (2006). Tractable models for information diffusion in social networks. In Knowledge discovery in databases: PKDD 2006 (pp. 259–271). Berlin Heidelberg: Springer.

  8. Kim, H., & Yoneki, E. (2012). Influential neighbours selection for information diffusion in online social networks. The 21st international conference on computer communication networks ICCCN 2012. IEEE (pp. 1–7).

  9. Weskida, M., & Michalski, R. (2016). Evolutionary algorithm for seed selection in social influence process. In IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) 2016 (pp. 1189–1196).

  10. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 1420–1443.

  11. Watts, D. J. (2002). A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences, 99(9), 5766–5771.

    Article  MathSciNet  MATH  Google Scholar 

  12. Newman, M. E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104.

    Article  MathSciNet  Google Scholar 

  13. Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Soongsil University, Seoul, Republic of Korea

    Duy-Linh Nguyen, Tri-Hai Nguyen, Trong-Hop Do & Myungsik Yoo

Authors
  1. Duy-Linh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tri-Hai Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trong-Hop Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Myungsik Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Myungsik Yoo.

Additional information

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1008) supervised by the IITP (Institute for Information and communications Technology Promotion).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, DL., Nguyen, TH., Do, TH. et al. Probability-Based Multi-hop Diffusion Method for Influence Maximization in Social Networks. Wireless Pers Commun 93, 903–916 (2017). https://doi.org/10.1007/s11277-016-3939-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3939-8

Keywords

Search

Navigation