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Title
Japanese: 
English:Extracting Multi-facet Community Structure from Bipartite Networks 
Author
Japanese: 鈴木 健太, 脇田 建.  
English: Kenta Suzuki, Ken Wakita.  
Language English 
Journal/Book name
Japanese: 
English:International Conference on Computational Science and Engineering 
Volume, Number, Page         pp. 312-319
Published date Aug. 2009 
Publisher
Japanese: 
English: 
Conference name
Japanese:International Conference on Computational Science and Engineering 
English: 
Conference site
Japanese:Vancouver, Canada 
English: 
DOI https://doi.org/10.1109/CSE.2009.451
Abstract Bipartite networks can represent various kinds of structures, dynamics, and interaction patterns found in social activities. M. E. J. Newman proposed a measure by which you can quantitatively evaluate the quality of network division, but his work is only applicable to uniform networks. This article extends his work and proposes a new modularity measure that can be applied to bipartite networks as well. Unlike the biparitite modularity measures previously proposed, the new measure acknowledges the fact that each individual in the society has more than just one aspect, and can thus be used to extract multi-faceted community structures from bipartite networks. The mathematical properties of the proposal is examined and compared with previous work. Empirical evaluation is conducted by using a data set synthesized from an artificial model and a real-life data set found in the field of ethnography.

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