Abstract
Influence Maximization has been applied to marketing, advertising and public opinion monitoring. Most of the existed influence maximization algorithms are greedy or heuristic algorithms which are too time consuming. Based on the observation that the structural hole nodes are much more influential, we develop structural holes theory-based influence maximization algorithm SG with an emphasis on time efficiency. We conduct experiments to verify our algorithm’s time efficiency and accuracy, the experimental results show that comparing with the existing algorithms, our algorithms are much faster and scalable.
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Acknowledgment
This work was supported in part by the National Science Foundation of China (61632010, 61100048, 61370222), the Natural Science Foundation of Heilongjiang Province (F2016034), the Education Department of Heilongjiang Province (12531498).
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Zhu, J., Yin, X., Wang, Y., Li, J., Zhong, Y., Li, Y. (2017). Structural Holes Theory-Based Influence Maximization in Social Network. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_73
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DOI: https://doi.org/10.1007/978-3-319-60033-8_73
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