Abstract
Information diffusion is the process of spreading information from one node to another over the network. To calculate the information diffusion coverage, it is important to assign propagation probability to every edge in the social network graph. Most popular models of information diffusion use Uniform Activation (UA) and Degree Weighted Activation (DWA) to calculate propagation probabilities. However, the results obtained by these methods are non-realistic. Therefore, we propose a new Activeness based Propagation Probability Initializer (APPI) model to obtain realistic information diffusion. This is achieved by assigning propagation probabilities based on activeness value inferred using topological node behavior. The experimental results show that APPI provides balanced and meaningful information diffusion coverage when compared with UA and DWA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. In: ACM Transactions on the Web (TWEB). ACM, New York, NY (2007)
Rogers, E.M.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)
Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM, New York, NY (2003)
Zhuang, H., Sun, Y., Tang, J., Zhang, J., Sun, X.: Influence maximization in dynamic social networks. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1313–1318. IEEE, Dallas, TX (2013)
Song, G., Li, Y., Chen, X., He, X., Tang, J.: Influential node tracking on dynamic social network: an interchange greedy approach. IEEE Trans. Knowl. Data Eng. 29(2), 359–372 (2017)
Li, W., Bai, Q., Zhang, M.: SIMiner: a stigmergy-based model for mining influential nodes in dynamic social networks. IEEE Trans. Big Data (2018)
Social Computing Research Homepage. http://socialnetworks.mpi-sws.org. Last accessed 6 April 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mithagari, A., Shankarmani, R. (2020). Activeness Based Propagation Probability Initializer for Finding Information Diffusion in Social Network. In: Dutta, D., Mahanty, B. (eds) Numerical Optimization in Engineering and Sciences. Advances in Intelligent Systems and Computing, vol 979. Springer, Singapore. https://doi.org/10.1007/978-981-15-3215-3_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-3215-3_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3214-6
Online ISBN: 978-981-15-3215-3
eBook Packages: EngineeringEngineering (R0)