Community detection; Graph clustering; Graph theory
Résumé :
[en] Community detection consists of grouping related vertices that usually show high intra-cluster connectivity and low inter-cluster connectivity. This is an important feature that many networks exhibit and detecting such communities can be challenging, especially when they are densely connected. The method we propose is a degenerate agglomerative hierarchical clustering algorithm (DAHCA) that aims at finding a community structure in networks. We tested this method using common classes of graph benchmarks and compared it to some state-of-the-art community detection algorithms.
Centre de recherche :
- Luxembourg Centre for Contemporary and Digital History (C2DH) > Digital History & Historiography (DHI) - Luxembourg Centre for Contemporary and Digital History (C2DH) > Doctoral Training Unit (DTU)
Disciplines :
Sciences informatiques
Auteur, co-auteur :
FISCARELLI, Antonio Maria ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
BELIAKOV, Aleksandr ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC)
KONCHENKO, Stanislav ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
A Degenerate Agglomerative Hierarchical Clustering Algorithm for Community Detection
Date de publication/diffusion :
2018
Nom de la manifestation :
10th Asian Conference on Intelligent Information and Database Systems
