Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
An open graph visualization system and its applications to software engineering
Software—Practice & Experience - Special issue on discrete algorithm engineering
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Topic Detection by Clustering Keywords
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Scalable Tensor Decompositions for Multi-aspect Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Hyperincident connected components of tagging networks
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Modularities for bipartite networks
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Exploit the tripartite network of social tagging for web clustering
Proceedings of the 18th ACM conference on Information and knowledge management
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Modularity for heterogeneous networks
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Collaborative tagging as a tripartite network
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Computer Science Review
Multiobjective hypergraph-partitioning algorithms for cut and maximum subdomain-degree minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Community detection is a branch of network analysis concerned with identifying strongly connected subnetworks. Social bookmarking sites aggregate datasets of often hundreds of millions of triples (document, user, and tag), which, when interpreted as edges of a graph, give rise to special networks called 3-partite, 3-uniform hypergraphs. We identify challenges and opportunities of generalizing community detection and in particular modularity optimization to these structures. Two methods for community detection are introduced that preserve the hypergraph's special structure to different degrees. Their performance is compared on synthetic datasets, showing the benefits of structure preservation. Furthermore, a tool for interactive exploration of the community detection results is introduced and applied to examples from real datasets. We find additional evidence for the importance of structure preservation and, more generally, demonstrate how tripartite community detection can help understand the structure of social bookmarking data.