Finding authorities and hubs from link structures on the World Wide Web
Proceedings of the 10th international conference on World Wide Web
Learning to Create Customized Authority Lists
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Getting our head in the clouds: toward evaluation studies of tagclouds
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The folksonomy tag cloud: when is it useful?
Journal of Information Science
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
The web as a graph: measurements, models, and methods
COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
On the selection of tags for tag clouds
Proceedings of the fourth ACM international conference on Web search and data mining
On kernel information propagation for tag clustering in social annotation systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Methodologies for improved tag cloud generation with clustering
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Tag cloud generation for results of multiple keywords queries
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.