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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-030-60259-8_14
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