Affiliations: [a] School of Computer Engineering and Science, Shanghai University, Shanghai, China
| [b] School of Computer Science and Technology, Kashi University, Xinjiang, China
| [c] School of Mathematics and Statistics, Kashi University, Xinjiang, China
| [d] School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China
Correspondence:
[*]
Corresponding author. Bofeng Zhang, School of Computer Engineering and Science, Shanghai University, Shanghai, China. E-mail: bfzhang@shu.edu.cn.
Abstract: A bipartite network is a special kind of complex network that consists of two different types of nodes with edges existing only between the different node types. There are numerous real-world examples of bipartite networks, such as scientific collaboration networks and film-actor networks, among many others. Detecting the community structure of bipartite networks not only contributes to a deeper understanding of their hidden structure, but also lays the foundation for research into the personalized recommendation technology. Most existing algorithms, however, only focus on the detection of non-overlapping community structures while ignoring overlapping community structures. In this study, we developed a micro-bipartite network model, Bi-EgoNet along with an algorithm called Overlapping Community Detection using Bi-EgoNet (OCDBEN). This algorithm first extracts the sub-bi-community set from each Bi-EgoNet using similarity within the bipartite network and then constructs a global community structure by merging the sub-bi-communities using the double-merger strategy. We evaluated the OCDBEN algorithm with several synthetic and real-world bipartite networks and compared it with existing state-of-the-art algorithms. The experimental results demonstrated that OCDBEN outperformed existing algorithms in both accuracy and effectiveness.
