Detecting Communities from Bipartite Networks Based on Bipartite Modularities
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
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
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Online social media such as delicious and digg are represented as tripartite networks whose vertices are users, tags, and resources. Detecting communities from such tripartite networks is practically important. Modularity is often used as the criteria for evaluating the goodness of network divisions into communities. For tripartite networks, Neubauer defines a tripartite modularity which extends Murata's bipartite modularity. However, Neubauer's tripartite modularity still uses projections and it will lose information that original tripartite networks have. This paper proposes new tripartite modularity for tripartite networks that do not use projections. Experimental results show that better community structures can be detected by optimizing our tripartite modularity.