PLDLS: A novel parallel label diffusion and label Selection-based community detection algorithm based on Spark in social networks
作者：
Highlights：
• A novel fast and accurate Spark-based parallel community detection algorithm is proposed.
• The proposed PLDLS algorithm uses label diffusion of core nodes along with a new label selection method.
• Multi-factor criteria for computing nodes importance is used to select core nodes.
• A fast and parallel merge phase is utilized to obtain more dense and accurate communities.
• The result of PLDLS completely is robust, stable and scalable in comparison with other examined methods.
摘要
•A novel fast and accurate Spark-based parallel community detection algorithm is proposed.•The proposed PLDLS algorithm uses label diffusion of core nodes along with a new label selection method.•Multi-factor criteria for computing nodes importance is used to select core nodes.•A fast and parallel merge phase is utilized to obtain more dense and accurate communities.•The result of PLDLS completely is robust, stable and scalable in comparison with other examined methods.
论文关键词：Parallel community detection,Label diffusion,Local similarity,Label selection,Spark,Social networks
论文评审过程：Received 11 October 2020, Revised 8 April 2021, Accepted 6 June 2021, Available online 11 June 2021, Version of Record 15 June 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115377
