An upper approximation based community detection algorithm for complex networks

作者：

Highlights：

• A rough set based community detection algorithm for complex networks has been proposed.

• Experiments have been performed on fourteen benchmark networks from diverse domains.

• Comparative analysis of the proposed algorithm has been performed with the relevant state-of-the-art methods.

• The performance of proposed algorithm is superior to state-of-the-art methods.

摘要

The emergence of multifarious complex networks has attracted researchers and practitioners from various disciplines. Discovering cohesive subgroups or communities in complex networks is essential to understand the dynamics of real-world systems. Researchers have made persistent efforts to investigate and infer community patterns in complex networks. However, real-world networks exhibit various characteristics wherein existing communities are not only disjoint but are also overlapping and nested. The existing literature on community detection consists of limited methods to discover co-occurring disjoint, overlapping and nested communities.

论文关键词：Community structure,Complex networks,Community detection algorithms,Overlapping communities,Neighborhood model,Rough sets

论文评审过程：Received 9 October 2016, Revised 5 February 2017, Accepted 19 February 2017, Available online 24 February 2017, Version of Record 4 April 2017.

论文官网地址：https://doi.org/10.1016/j.dss.2017.02.010