Abstract
Social network influence maximization algorithm can be widely applied in social marketing, public sentiment control and other relevant fields. This paper focuses on two major problems local structure characteristics of the network which was ignored by previous work and the nodes influence overlapping problems. To deal with the problems, we propose a new influence maximization algorithm for social networks based on local structure characteristics. In this algorithm, the topological potential is used to measure the influence of the nodes to divide communities. The candidate global influence nodes are selected from the community according to the measurements then the local edge deduplication is applied to complete the seed nodes selection. Finally, we conduct extensive experiments to evaluate the feasibility and effectiveness of the algorithm, which, as the results, has better performance in propagation effect compared with the state of the arts.
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Acknowledgements
This work was supported by The Youth Foundation of Heilongjiang Province of China under Grant No. QC2016083, the Innovative Talents Research Special Funds of Harbin Science and Technology Bureau under Grant No. 2016RQQXJ128, The Fundamental Research Funds for the Central Universities under Grant No. HEUCF180606, and the National Natural Science Foundation of China under Grant No. 61672179, 61370083 and 61402126.
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Wang, Y., Zhang, B., Shi, J., Yang, J., Zhang, J. (2018). An Algorithm of Influence Maximization in Social Network Based on Local Structure Characteristics. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_36
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DOI: https://doi.org/10.1007/978-3-319-99247-1_36
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