# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
###### Abstract  
Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure's merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density \(D\). And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network's modularity. overlapping community; bipartite network; community detection; community density +
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mining provides a new research basis and method for the epidemic model [7]. In the field of recommendation, the research on the community structure detection of the bipartite network can provide a new method for the recommendation technology of bipartite network systems. For example, in the bipartite network recommendation system of e-commerce users and commodities, if users are divided according to their characteristics, users with different characteristics are divided into different communities, and products are recommended for users according to the relationship between users in the communities, then the recommended products will be more in line with the needs of users. According to the subordinate relationship of nodes, community structure can be divided into overlapping community structure and non-overlapping community structure. Traditional community detection algorithms mostly focus on the non-overlapping communities in the bipartite network, while few studies focus on the overlapping communities in the bipartite network. But in the real world, a node usually has more than one attribute. A node can belong to more than one community. For example, in the scientific collaboration network [5], various authors can write a paper, and an author can sign various papers. Therefore, it has more crucial practical value and scientific research value to study the detection of bipartite network overlapping communities. Recently, with more and more researchers going deep into the field of bipartite network community detection, quite a few algorithms have emerged in the field of community detection [7; 8; 9; 10; 11]. In general, these algorithms can be divided into two categories. One is to project the bipartite network to the one-mode network [12; 13; 14]; the advantages of this approach can be based on the current research results of the one-mode network, which is easier to operate. However, in projecting to the one-mode network, the bipartite network would lose some original information and increase the noise. The other is to directly detect the community on the bipartite network [15]. Compared with the projection of the bipartite network, it is more challenging to operate. Still, the original network structure information can be protected to the greatest extent not to be destroyed in the projection process. However, this kind of approach mainly starts from the network's topology structure and carries out community detection from the macro perspective. It takes little consideration of the relationship between nodes and fails to discover some microscopic structural features of the bipartite network. How to measure the merits of community detection algorithm results is another crucial problem.