Quasi-Clique Mining for Graph Summarization
Type de document :
Communication dans un congrès avec actes
Titre :
Quasi-Clique Mining for Graph Summarization
Auteur(s) :
Castillon, Antoine [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Graphes, AlgOrithmes et AppLications [GOAL]
Baste, Julien [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Seba, Hamida [Auteur]
Graphes, AlgOrithmes et AppLications [GOAL]
Haddad, Mohammed [Auteur]
Graphes, AlgOrithmes et AppLications [GOAL]
Operational Research, Knowledge And Data [ORKAD]
Graphes, AlgOrithmes et AppLications [GOAL]
Baste, Julien [Auteur]

Operational Research, Knowledge And Data [ORKAD]
Seba, Hamida [Auteur]
Graphes, AlgOrithmes et AppLications [GOAL]
Haddad, Mohammed [Auteur]
Graphes, AlgOrithmes et AppLications [GOAL]
Titre de la manifestation scientifique :
Database and Expert Systems Applications. DEXA 2022
Ville :
Vienne
Pays :
Autriche
Date de début de la manifestation scientifique :
2022-08-22
Titre de la revue :
Lecture Notes in Computer Science
Éditeur :
Springer International Publishing
Date de publication :
2022-07-29
Mot(s)-clé(s) en anglais :
Graph summarization
quasi-clique mining
quasi-clique mining
Discipline(s) HAL :
Informatique [cs]/Algorithme et structure de données [cs.DS]
Résumé en anglais : [en]
Several graph summarization approaches aggregate dense sub-graphs into super-nodes leading to a compact summary of the input graph. The main issue for these approaches is how to achieve a high compression rate while retaining ...
Lire la suite >Several graph summarization approaches aggregate dense sub-graphs into super-nodes leading to a compact summary of the input graph. The main issue for these approaches is how to achieve a high compression rate while retaining as much information as possible about the original graph structure within the summary. These approaches necessarily involve an algorithm to mine dense structures in the graph such as quasi-clique enumeration algorithms. In this paper, we focus on improving these mining algorithms for the specific task of graph summarization. We first introduce a new pre-processing technique to speed up this mining step. Then, we extend existing quasi-clique enumeration algorithms with this filtering technique and apply them to graph summarization.Lire moins >
Lire la suite >Several graph summarization approaches aggregate dense sub-graphs into super-nodes leading to a compact summary of the input graph. The main issue for these approaches is how to achieve a high compression rate while retaining as much information as possible about the original graph structure within the summary. These approaches necessarily involve an algorithm to mine dense structures in the graph such as quasi-clique enumeration algorithms. In this paper, we focus on improving these mining algorithms for the specific task of graph summarization. We first introduce a new pre-processing technique to speed up this mining step. Then, we extend existing quasi-clique enumeration algorithms with this filtering technique and apply them to graph summarization.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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