Quasi-Clique Mining for Graph Summarization

Castillon, Antoine; Baste, Julien; Seba, Hamida; Haddad, Mohammed

Document type :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-031-12426-6_29

Title :

Quasi-Clique Mining for Graph Summarization

Author(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]

Conference title :

Database and Expert Systems Applications. DEXA 2022

City :

Vienne

Country :

Autriche

Start date of the conference :

2022-08-22

Journal title :

Lecture Notes in Computer Science

Publisher :

Springer International Publishing

Publication date :

2022-07-29

English keyword(s) :

Graph summarization
quasi-clique mining

HAL domain(s) :

Informatique [cs]/Algorithme et structure de données [cs.DS]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

COmpression de REseaux et de GRAPHes pour une Informatique Efficace

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

document
Open access
Access the document

Extended_Version_preprint.pdf
Open access
Access the document

document
Open access
Access the document

Extended_Version_preprint.pdf
Open access
Access the document

Quasi-Clique Mining for Graph Summarization BibTeX CSV Excel RIS

Files

Quasi-Clique Mining for Graph Summarization

BibTeX

CSV

Excel

RIS