Extracting Multi-facet Community Structure from Bipartite Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Detecting Communities from Bipartite Networks Based on Bipartite Modularities
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Structure of Heterogeneous Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Identifying overlapping communities in folksonomies or tripartite hypergraphs
Proceedings of the 20th international conference companion on World wide web
Extracting the mesoscopic structure from heterogeneous systems
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Detecting overlapping communities in folksonomies
Proceedings of the 23rd ACM conference on Hypertext and social media
Detecting communities in K-partite K-uniform (hyper)networks
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Tripartite community structure in social bookmarking data
The New Review of Hypermedia and Multimedia - Special issue on Social Linking and Hypermedia
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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. Although Newman-Girvan modularity is popular for unipartite networks, it is not suitable for n-partite networks. For bipartite networks, Barber, Guimera, Murata and Suzuki define bipartite modularities. For tripartite networks, Neubauer defines 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.