IEDC: An integrated approach for overlapping and non-overlapping community detection
作者：
Highlights：
• A novel algorithm is proposed to discover the overlapping and non-overlapping community structures.
• A primary node based criterion is presented based on the internal and external association degrees.
• Providing an extensive simulation studies to testify the proposed approach.
• Experimental results are performed on benchmark network data-sets.
• The proposed approach outperformed several state-of-the-art methods.
摘要
•A novel algorithm is proposed to discover the overlapping and non-overlapping community structures.•A primary node based criterion is presented based on the internal and external association degrees.•Providing an extensive simulation studies to testify the proposed approach.•Experimental results are performed on benchmark network data-sets.•The proposed approach outperformed several state-of-the-art methods.
论文关键词：Community detection,Unsupervised learning,Social networks,Normalized mutual information,Overlapping connectivity structures,Non-overlapping community structures,Network communities
论文评审过程：Received 5 June 2016, Revised 12 February 2017, Accepted 13 February 2017, Available online 14 February 2017, Version of Record 27 March 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.02.018
