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Influence maximization algorithm based on cross propagation in location-based social networks

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Abstract

The problem of influence maximization is one of the key issues in social networks. Most of the current studies focus on online social networks while ignoring offline interpersonal relationship networks. Fortunately, the cross propagation considers the characteristics of both the online social networks and offline interpersonal relationship networks, which is more suitable for the real scenarios. In this paper, we design a cross propagation model based on location-based social networks to establish a connection between online social networks and offline interpersonal relationship networks. Where the offline interpersonal relationships are mined by the similarity of POIs, which are based on the encounter characteristics. Then, an influence maximization algorithm based on cross propagation model is provided. The simulation results indicate that the propagation effect of influence in cross propagation networks is better than that only in online social networks, and the proposed algorithm has higher performances in terms of the running time and the sphere of influence.

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References

  1. Alhajj, R., & Rokne, J. (2014). Encyclopedia of social network analysis and mining. New York: Springer. https://doi.org/10.1007/978-1-4614-6170-8.

    Book  MATH  Google Scholar 

  2. Misner, I. R. (1994). The world’s best known marketing secret: Building your business with word-of-mouth marketing. Austin: Bard & Stephen.

    Google Scholar 

  3. Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics & Informatics, 34(7), 1177–1190.

    Article  Google Scholar 

  4. Wen, Y.-T., Lei, P.-R., Peng, W.-C., & Zhou, X.-F. (2014). Exploring social influence on location-based social networks. In 2014 IEEE international conference on data mining (pp. 1043–1048). IEEE. https://doi.org/10.1109/ICDM.2014.66.

  5. Zhou, T., Cao, J., Liu, B., Xu, S., Zhu, Z., & Luo, J. (2015). Location-based influence maximization in social networks. In Proceedings of the 24th ACM international on conference on information and knowledge management (pp. 1211–1220). ACM. https://doi.org/10.1145/2806416.2806462.

  6. Li, J., Cai, Z., Yan, M., & Li, Y. (2016). Using crowdsourced data in location-based social networks to explore influence maximization. In IEEE INFOCOM 2016-the 35th annual IEEE international conference on computer communications (pp. 1–9). IEEE. https://doi.org/10.1109/INFOCOM.2016.7524471

  7. Chen, S., Ju, F., Li, G., Feng, J., Tan, K. L., & Tang, J. (2015). Online topic-aware influence maximization. Proceedings of the Vldb Endowment, 8(6), 666–677.

    Article  Google Scholar 

  8. Gomez-Rodriguez, M., Le, S., Nan, D., & Zha, H. (2016). Influence estimation and maximization in continuous-time diffusion networks. ACM Transactions on Information Systems, 34(2), 1–33.

    Article  Google Scholar 

  9. Jendoubi, S., Martin, A., Litard, L., Hadji, H. B., & Yaghlane, B. B. (2017). Two evidential data based models for influence maximization in twitter. Knowledge-Based Systems, 121, 58–70. https://doi.org/10.1016/j.knosys.2017.01.014.

    Article  Google Scholar 

  10. Wen, Z., Kveton, B., Valko, M., & Vaswani, S. (2017). Online influence maximization under independent cascade model with semi-bandit feedback. In Advances in neural information processing systems 30 proceedings. arXiv:1605.06593

  11. Kempe, D., Kleinberg, J. M., & Tardos, É. (2005). Influential nodes in a diffusion model for social networks. In 32nd international colloquium on automata, languages and programming (pp. 1127–1138). Springer. https://doi.org/10.1007/11523468_91.

  12. MacKay, D. J. (1998). Introduction to Monte Carlo methods. In M. I. Jordan (Ed.), Learning in graphical models (pp. 175–204). Dordrecht: Springer. https://doi.org/10.1007/978-94-001-5014-9_7.

    Chapter  Google Scholar 

  13. 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. https://doi.org/10.1145/1281192.1281239.

  14. 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. https://doi.org/10.1145/1557019.1557047.

  15. 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. https://doi.org/10.1145/1963192.1963217.

  16. Chen, W., Yuan, Y., & Zhang, L. (2010). Scalable influence maximization in social networks under the linear threshold model. In IEEE international conference on data mining (pp. 88–97). IEEE. https://doi.org/10.1109/IDCM.2010.118.

  17. Tarameshloo, E., Loorak, M. H., Fong, P. W., & Carpendale, S. (2016). Using visualization to explore original and anonymized lbsn data. In Computer graphics forum (Vol. 35, No. 3, pp. 291–300). Wiley Online Library. https://doi.org/10.1111/cgf.12905.

  18. Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In T. Imielinski & H. F. Korth (Eds.), Mobile computing (pp. 153–181). Boston: Springer. https://doi.org/10.1007/978-0-585-29603-6_5.

    Chapter  Google Scholar 

  19. Yang, Y., Xu, Y., Wang, E., Lou, K., & Luan, D. (2018). Exploring influence maximization in online and offline double-layer propagation scheme. Information Sciences, 450, 182–199. https://doi.org/10.1016/j.ins.2018.03.048.

    Article  Google Scholar 

  20. Leskovec, J., & Krevl, A. (2014). {SNAP Datasets}:{Stanford} large network dataset collection. http://snap.stanord.edu/data.

  21. Qiao, X., Yu, W., Zhang, J., Tan, W., Su, J., Xu, W., et al. (2015). Recommending nearby strangers instantly based on similar check-in behaviors. IEEE Transactions on Automation Science and Engineering, 12(3), 1114–1124.

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (61262089).

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Ürümqi, China

    Zhen Zhang

  2. Key Laboratory of Multilingual Information Technology of Xinjiang, Xinjiang University, Ürümqi, China

    Zhenyu Zhang & Xiaohong Wu

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  1. Zhen Zhang
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  2. Zhenyu Zhang
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  3. Xiaohong Wu
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Correspondence to Zhen Zhang.

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Zhang, Z., Zhang, Z. & Wu, X. Influence maximization algorithm based on cross propagation in location-based social networks. Wireless Netw 26, 5035–5046 (2020). https://doi.org/10.1007/s11276-020-02335-x

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