Abstract
Influence maximization problem focuses on finding a certain number of influential people to make their influence maximized in social networks. As positive influence has more practical significance in the viral marketing, we propose Positive Influence Maximization (PIM) problem and apply it in signed social networks. Considering the attitude of users to products, we propose a new propagation model named Linear Threshold model with Attitude (LT-A). In the model, each node has a new parameter η which denotes the attitude of node, and each edge has a new parameter ρ which denotes the relationships between nodes. We prove the PIM problem is NP-hard and the influence spread function is monotonous and submodular. Therefore, we use a greedy algorithm to obtain a solution with an approximation ratio of (1 − 1/e). Extensive experiments are conducted on two real-world network datasets and experimental results show that we can achieve higher influence spread than other existing approaches by using our model.
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Wang, H., Yang, Q., Fang, L., Lei, W. (2015). Maximizing Positive Influence in Signed Social Networks. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_30
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DOI: https://doi.org/10.1007/978-3-319-27051-7_30
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