Modularity for heterogeneous networks
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Extracting the mesoscopic structure from heterogeneous systems
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Detecting communities in K-partite K-uniform (hyper)networks
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Analysis of Neural Network of C.elegans by Converting into Bipartite Network
International Journal of Artificial Life Research
Dense subgraph mining with a mixed graph model
Pattern Recognition Letters
Detecting community structure in bipartite networks based on matrix factorisation
International Journal of Wireless and Mobile Computing
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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.