Abstract
Considering the existence of competition in the process of social network communication, and the change of sensitivity in the process of communication, this paper proposes a new relative influence weight function that combining with the existing linear threshold model, the sensitivity of information, and the threshold characteristic of the node, namely, URLT model. Which can measure the information communication ability. By simulating the spread of different networks, different sensitivity information and different node thresholds, comparing the final propagation situation, the experimental results show that the final influence range is consistent with the real spread situation. Therefore, the model has some reference value for the discovery and suppression of the law of information dissemination.
Keywords
- Relative Influential Weights
- Linear Threshold Model
- Information Dissemination
- Node-specific Thresholds
- Public Opinion Guidance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, T., Zhong, Y., Chen, K.: Interdisciplinary study on popularity prediction of social classified hot online events in China. Telematics Inform. 34(3), 755–764 (2017)
Jung, S.H., Kim, J.: A new way of extending network coverage: relay-assisted D2D communications in 3GPP. ICT Express 2(3), 117–121 (2016)
Rasshotte, L.: Social Influence: The Blackwell Encyclopedia of Social Psychology, vol. IX. Blackwell Publishing, Malden (2007)
Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Sience 337(6092), 337–341 (2012)
Xindong, W., et al.: Analysis of the influence of online social networks. J. Comput. Sci. 37(4), 735–751 (2015)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)
Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)
Freeman, L.C.: A set of measures of centrality based on betweenss. Socialmetry 40(1), 35–41 (1977)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence in a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA, pp. 137–146 (2003)
Gruhl, D., Guha, R., Liben-Nowell, D., Tom-kins, A.: Information diffusion though blogspace. In: Proceeding of the 13th International Conference on World Wide Web, pp. 491–501 (2004)
Chen, W., Yuan, Y., Zhang, L.: Scalable in-fluence maximization in social networks under the liner threshold model. In: Proceedings of the 2010 IEEE International Conference on Data Mining, pp. 88–97 (2010)
Chen, W., Wang, C., Wang, Y.: 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, pp. 1029–1038 (2010)
Jia-tang, T., Yi-tong, W., Xiao-jun, F.: A new hybrid algorithm for influence maximization in social networks. Chin. J. Comput. 34(10), 1956–1964 (2011)
Long, W.J.: An online social network information propagation model based on relative weight of users. J. Phys. 64(5), 050501 (2015)
Bicheng, L., Feng, D., et al.: Analysis of Network Public Opinion. Theory Technology and Application Strategy. National Defense Industry Press, Beijing (2015)
Acknowledgement
This work is supported by the National Cryptography Development Fund of China Under Grants No. MMJJ20170112, National Key Research and Development Program of China Under Grants No. 2017YFB0802000, National Nature Science Foundation of China (Grant Nos. 61772550, U1636114), the Natural Science Basic Research Plan in Shaanxi Province of china (Grant Nos. 2016JQ6037) and Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201610).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Zhang, D., Wang, X.A., Li, X., Xu, C. (2018). Threshold Model Based on Relative Influence Weight of User. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-75928-9_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75927-2
Online ISBN: 978-3-319-75928-9
eBook Packages: EngineeringEngineering (R0)