摘要
Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network （named IERCP） is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.
Projection is a widely used method in bipartite networks. However, each projection has a specific application scenario and differs in the forms of mapping for bipartite networks. In this paper, inspired by the network-based information exchange dynamics, we propose a uniform framework of projection. Subsequently, an information exchange rate projection based on the nature of community structures of a network （named IERCP） is designed to detect community structures of bipartite networks. Results from the synthetic and real-world networks show that the IERCP algorithm has higher performance compared with the other projection methods. It suggests that the IERCP may extract more information hidden in bipartite networks and minimize information loss.
基金
Project supported by the National Natural Science Foundation of China(Grant Nos.11505114 and 10975099)
the Program for Professor of Special Appointment(Orientational Scholar)at Shanghai Institutions of Higher Learning(Grant Nos.QD02015016 and DUSST02)
the Shanghai Project for Construction of Discipline Peaks,the Natural Science Foundation of Guangxi Zhuang Guangxi Zhuang Autonomous Region(Grant No.2016GXNSFDA380031)
the Fundamental Ability Enhancement Project for Young and Middle-aged University Teachers in Guangxi Zhuang Autonomous Region(Grant No.2017KY0859)
