Abstract
Since the steady development of online social networks, it has resulted in wide-spread research works in the social network analytical areas, especially in the area of influence maximization. Previous works study the influence propagation process based on the social influence model. But traditional influence models ignore some important aspect of influence propagation. The drawbacks of the models are the simplistic influence diffusion process, and the models lack attitude states and capability to capture the interaction between users. To address these problems, we modify Linear Threshold model based on multi-level attitude and users’ interaction, which is proposed modeling the positive and negative attitude towards an entity in the signed social network and the effect of interaction relationship between users. Then we propose the LT-MLA greedy algorithm to solve the positive influence maximization problem. Finally, we conducted experiments on three real-world data sets to select initial k seed with the positive attitude. The results show that the proposed solution in this paper performs better than other heuristic algorithms.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China under Grant Numbers 61632009, 61472451 and 61272151, and the High Level Talents Program of Higher Education in Guangdong Province under Funding Support Number 2016ZJ01.
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Wang, F., Wang, G., Xie, D. (2016). Maximizing the Spread of Positive Influence Under LT-MLA Model. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_34
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DOI: https://doi.org/10.1007/978-3-319-49178-3_34
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