Community detection; Graph clustering; Graph theory
Abstract :
[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.
Research center :
- 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 :
Computer science
Author, co-author :
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)
External co-authors :
no
Language :
English
Title :
A Degenerate Agglomerative Hierarchical Clustering Algorithm for Community Detection
Publication date :
2018
Event name :
10th Asian Conference on Intelligent Information and Database Systems
