Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.With the development of complex network in various fields including biological organisms and human society, community detection has become a vital step to understand the structure and dynamics of networks [1][2][3] . Although no common definition of community has been agreed upon, it is widely accepted that a community should have more internal than external connections 4 . However, many real networks have communities with pervasive overlaps [5][6][7] . For example, a person belongs to more than one social group such as family group and friend group. So, these objects should be divided into multiple groups, which are known as overlapping nodes. The aim of overlapping community detection is to discover such overlapping nodes and communities.In the past few years, many different approaches, such as hierarchical clustering 8 , spectral clustering 9,10 and optimization based algorithms 11,12 have been proposed to uncover community structure in networks. These methods restrict a node to belonging to only one community and therefore result in some computational advantages. However, for real networks having complex overlapping community structures, these methods are obviously inadequate in identifying communities with overlaps 5 . For this reason, overlapping community detection has drawn lots of attention. Generally speaking, existing overlapping community detection approaches could be divided into two categories: node based algorithms (node clustering) and link based algorithms (link clustering).The node based overlapping community detection algorithms divide nodes of the network into different communities directly, utilizing the structure information of nodes. Many...
