KGraph: isolated and highly connected community detection in graphs

Muñoz, Héctor and Vicente Cestero, Eloy and González, Ignacio and Mateos Caballero, Alfonso and Jiménez Martín, Antonio (2018). KGraph: isolated and highly connected community detection in graphs. In: "15th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2018)", 15-18 Oct 2018, Palma de Mallorca, España. ISBN 978-84-09-05005-5. pp. 214-225.

Description

Title: KGraph: isolated and highly connected community detection in graphs
Author/s:
  • Muñoz, Héctor
  • Vicente Cestero, Eloy
  • González, Ignacio
  • Mateos Caballero, Alfonso
  • Jiménez Martín, Antonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: 15th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2018)
Event Dates: 15-18 Oct 2018
Event Location: Palma de Mallorca, España
Title of Book: USB Proceedings the 15th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2018)
Date: 2018
ISBN: 978-84-09-05005-5
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainMTM2017-86875-C3-3-RUnspecifiedUniversidad Politécnica de MadridToma de decisiones multicriterio y modelos de interdependencia para la gestión de riesgos. Seguridad en ATM
Government of SpainMTM2014-56949-C3-2-RUnspecifiedUniversidad Politécnica de MadridApoyo a decisiones en análisis de riesgos. Seguridad operacional aérea

More information

Item ID: 54731
DC Identifier: http://oa.upm.es/54731/
OAI Identifier: oai:oa.upm.es:54731
Official URL: http://mayor2.dia.fi.upm.es/dasg/sites/default/files/images/archivos/community-detection.pdf
Deposited by: Memoria Investigacion
Deposited on: 08 May 2019 09:12
Last Modified: 14 May 2019 12:01
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