Memorias de investigación
Ponencias en congresos:
KGraph: Isolated and highly connected community detection in graphs
Año:2018
Áreas de investigación
  • Inteligencia artificial,
  • Investigación operativa
Datos
Descripción
Current network analysis algorithms are of seminal importance because they are able to detect patterns in networks with a variety of classes and sizes that are of vital importance in multiple fields of research. Highly connected communities whose vertices are closely related to each other and have only a few external relations are one such key pattern. Most community detection algorithms in graphs guarantee that each community is relatively isolated but not highly cohesive. This can generate many irrelevant communities for large networks. In this paper we propose KGraph, an efficient highly connected community detection algorithm that takes a density-based approach using the k-core of the graph and can also establish a community hierarchy for improved visualization. KGraph has been compared with Dengraph, the best-known density-based algorithm in the literature both run on the Netscience network.
Internacional
Si
Nombre congreso
15th International Conference on Modeling Decisions for Artificial Intelligence
Tipo de participación
960
Lugar del congreso
Mallorca, España
Revisores
Si
ISBN o ISSN
978-84-09-05005-5
DOI
Fecha inicio congreso
15/10/2018
Fecha fin congreso
18/10/2018
Desde la página
214
Hasta la página
225
Título de las actas
Proceedings of the 15th International Conference on Modeling Decisions for Artificial Intelligence
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial