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- W2893199675 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." @default.
- W2893199675 created "2018-10-05" @default.
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- W2893199675 date "2018-01-01" @default.
- W2893199675 modified "2023-09-28" @default.
- W2893199675 title "A Hierarchy Based Influence Maximization Algorithm in Social Networks" @default.
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- W2893199675 doi "https://doi.org/10.1007/978-3-030-01421-6_42" @default.
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