TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks
作者：
Highlights：
• We propose a two-stage iterative framework for influence maximization in social networks (ie., TIFIM). It combines the spread benefit with selection of seed nodes, guaranteeing the remarkable efficiency as well as high accuracy.
• Based on the last iteration results and the two-hop measure, we put forward an efficient FLAS to calculate spread benefit of each node, further improving the efficiency and accuracy of TIFIM.
• We define the apical dominance to describe the overlapping phenomenon among nodes. We further propose RAD to determine the seed nodes from candidate nodes.
摘要
•We propose a two-stage iterative framework for influence maximization in social networks (ie., TIFIM). It combines the spread benefit with selection of seed nodes, guaranteeing the remarkable efficiency as well as high accuracy.•Based on the last iteration results and the two-hop measure, we put forward an efficient FLAS to calculate spread benefit of each node, further improving the efficiency and accuracy of TIFIM.•We define the apical dominance to describe the overlapping phenomenon among nodes. We further propose RAD to determine the seed nodes from candidate nodes.
论文关键词：Social networks,Influence maximization,Two-stage selection,Iterative framework
论文评审过程：Received 3 October 2018, Revised 4 February 2019, Accepted 18 February 2019, Available online 4 March 2019, Version of Record 4 March 2019.
论文官网地址：https://doi.org/10.1016/j.amc.2019.02.056
