Abstract
With the rapid development of social networks, discovering the propagation mechanism of information has become one of the key issues in social network analysis, which has attracted great attention. The existing propagation models only take into account individual influence between users and their neighbors, ignoring that different topologies formed by neighbors will have different influence on the target user. In this paper, we combine the influence of neighbor structure on different topics with the distribution of user interest on different topics, propose an propagation model based on structure influence and topic-aware interest, called NSTI-IC. We use an expectation maximization algorithm and a gradient descent algorithm to learn parameters of NSTI-IC. The experimental results on real datasets show that NSTI-IC model is superior to classical IC and structInf-IC models in terms of MSE and accuracy.
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
Similar content being viewed by others
References
Huang, H., Dong, Y., et al.: Will triadic closure strengthen ties in social networks? ACM Trans. Knowl. Disc. Data (TKDD) 12(3), 1–25 (2018)
Kempe, D., Kleinberg, J., et al.: 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 (2003)
Liu, Y., Xie, S., Zhong, Z., Li, J., Ren, Q.: Research on topic-interest based influence maximization algorithm in social networks. J. Comput. Res. Dev. 55(11), 2406–2418 (2018)
Rizi, F.S., Granitzer, M.: Predicting event attendance exploring social influence. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 2131–2134 (2019)
Ugander, J., et al.: Structural diversity in social contagion. In: Proceedings of the National Academy of Sciences, pp. 5962–5966 (2012)
Wang, X., et al.: NNMLInf: social influence prediction with neural network multi-label classification. In: Proceedings of the ACM Turing Celebration Conference-China, pp. 1–5 (2019)
Wu, X., Fu, L., et al.: Collective influence maximization. In: Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 385–386 (2019)
Zang, C., Cui, P., et al.: Quantifying structural patterns of information cascades. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 867–868 (2017)
Zang, C., Cui, P., et al.: Structural patterns of information cascades and their implications for dynamics and semantics. arXiv preprint arXiv:1708.02377 (2017)
Zhang, J., Tang, J., et al.: Structinf: mining structural influence from social streams. In: Proceedings of the 31th AAAI Conference on Artificial Intelligence (2017)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61972135, No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. LH2020F043), and the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, C., Yin, Y., Liu, Y. (2020). NSTI-IC: An Independent Cascade Model Based on Neighbor Structures and Topic-Aware Interest. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-60259-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60258-1
Online ISBN: 978-3-030-60259-8
eBook Packages: Computer ScienceComputer Science (R0)