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Link prediction in very large directed graphs: Exploiting hierarchical properties in parallel

Autor
Garcia-Gasulla, D.; Cortes, U.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
3rd International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data
Any de l'edició
2014
Data de presentació
2014-05
Llibre d'actes
Proceedings of the 3rd Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 11th Extended Semantic Web Conference (ESWC 2014): Crete, Greece, May 25, 2014
Pàgina inicial
1
Pàgina final
13
Editor
CEUR-WS.org
Repositori
http://hdl.handle.net/2117/26091 Obrir en finestra nova
URL
http://ceur-ws.org/Vol-1243/paper5.pdf Obrir en finestra nova
Resum
Link prediction is a link mining task that tries to find new edges within a given graph. Among the targets of link prediction there is large directed graphs, which are frequent structures nowadays. The typical sparsity of large graphs demands of high precision predictions in order to obtain usable results. However, the size of those graphs only permits the execution of scalable algorithms. As a trade-off between those two problems we recently proposed a link prediction algorithm for directed gra...
Citació
García-Gasulla, D.; Cortés, C. Link prediction in very large directed graphs: Exploiting hierarchical properties in parallel. A: International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data. "Proceedings of the 3rd Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data co-located with 11th Extended Semantic Web Conference (ESWC 2014): Crete, Greece, May 25, 2014". Creta: CEUR-WS.org, 2014, p. 1-13.
Paraules clau
Algorithms, Computational Complexity, Data Mining, Economic And Social Effects, Forecasting, Graphic Methods, Semantic Web, Statistical Tests
Grup de recerca
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants

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