%A Liqing Qiu,Wei Jia,Xin Fan %T Influence Maximization Algorithm Based on Overlapping Community %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2018.1389 %P 94-102 %V 3 %N 7 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4688.shtml} %8 2019-07-25 %X

[Objective] This paper proposes a new algorithm for influence maximization based on overlapping community, called IM-BOC algorithm, aiming to the low efficiency of greedy algorithm. [Methods] This method selects candidate seed set by combing propagation degree and k-core firstly, then it utilizes CELF algorithm to ensure the optimal seed set, which can improve both efficiency and accuracy. [Results] The experimental results show that running time of our algorithm can improve about 89% when facing Amazon dataset. [Limitations] Our IM-BOC algorithm allocates the number of candidate seeds only according to the number of community nodes, which has insufficient theoretical evidence. [Conclusions] IM-BOC algorithm is applicable to large scale networks under the premise of ensuring the influence spread.