<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Yiannis Kompatsiaris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A graph-based clustering scheme for identifying related tags in folksonomies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 12th international conference on Data warehousing and knowledge discovery</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">DaWaK’10</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">folksonomies</style></keyword><keyword><style  face="normal" font="default" size="100%">graph-based clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">tag recommendation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin, Heidelberg</style></pub-location><pages><style face="normal" font="default" size="100%">65–76</style></pages><isbn><style face="normal" font="default" size="100%">3-642-15104-3, 978-3-642-15104-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents a novel scheme for graph-based clusteringwith the goal of identifying groups of related tags in folksonomies.The proposed scheme searches for core sets, i.e. groups of nodes thatare densely connected to each other by efficiently exploring the twodimensional 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.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">A Graph-Based Clustering Scheme for Identifying Related Tags in Folksonomies</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents a novel scheme for graph-based clusteringwith the goal of identifying groups of related tags in folksonomies.The proposed scheme searches for core sets, i.e. groups of nodes thatare densely connected to each other by efficiently exploring the twodimensional 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.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Theodosiou, Theodosios</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Thomopoulos, G. N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gene functional annotation by statistical analysis of biomedical articles</style></title><secondary-title><style face="normal" font="default" size="100%">I. J. Medical Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">76</style></volume><pages><style face="normal" font="default" size="100%">601-613</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">GRANULAR GRAPH CLUSTERING IN THE WEB</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We investigate the partition of a weighted graph, representing traffic, to a number ofsubgraphs such that both inter(external)-subgraph traffic is minimized and intra(internal)-subgraph traffic is maximized. The long-term objective is Web-navigation support. Wepursue a solution by applying a simple agglomerative clustering algorithm, or ACA forshort, to a metric space emerging from a weighted graph. An enabling technology isinspired from mathematical lattice theory. The proposed techniques compare favorablywith other techniques in an application to a graph stemming from a University Web-site.&lt;/p&gt;
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