Abstract
Influence maximization refers to mining top-K most influential nodes from a social network to maximize the final propagation of influence in the network, which is one of the key issues in social network analysis. It is a discrete optimization problem and is also NP-hard under both independent cascade and linear threshold models. The existing researches show that although the greedy algorithm can achieve an approximate ratio of \( \left( {1 - 1/e} \right) \), its time cost is expensive. Heuristic algorithms can improve the efficiency, but they sacrifice a certain degree of accuracy. In order to improve efficiency without sacrificing much accuracy, in this paper, we propose a new approach called Hierarchy based Influence Maximization algorithm (HBIM in short) to mine top-K influential nodes. It is a two-phase method: (1) an algorithm for detecting information diffusion levels based on the first-order and second-order proximity between social nodes. (2) a dynamic programming algorithm for selecting levels to find influential nodes. Experiments show that our algorithm outperforms the benchmarks.
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Acknowledgments
The research was supported in part by National Basic Research Program of China (973 Program, No. 2013CB329605).
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Li, L., Li, K., Xiang, C. (2018). A Hierarchy Based Influence Maximization Algorithm in Social Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_42
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DOI: https://doi.org/10.1007/978-3-030-01421-6_42
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