A graph-based clustering scheme for identifying related tags in folksonomies

Authors:
Symeon Papadopoulos;Yiannis Kompatsiaris;Athena Vakali
Affiliations:
Informatics and Telematics Institute, CERTH, Thessaloniki, Greece and Department of Informatics, Aristotle University, Thessaloniki, Greece;Informatics and Telematics Institute, CERTH, Thessaloniki, Greece;Department of Informatics, Aristotle University, Thessaloniki, Greece
Venue:
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Year:
2010

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Abstract

The paper presents a novel scheme for graph-based clustering with the goal of identifying groups of related tags in folksonomies. The proposed scheme searches for core sets, i.e. groups of nodes that are densely connected to each other by efficiently exploring the two-dimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag networks by assessing the relatedness of same-cluster tags and by using tag clusters for tag recommendation. In addition, we compare our results to the ones derived from a baseline graph-based clustering method and from a popular modularity maximization clustering method.