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Please use this identifier to cite or link to this item: http://hdl.handle.net/10016/14450

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Title: Using the ACO algorithm for path searches in social networks
Author(s): Rivero Espinosa, Jesica
Cuadra, Dolores
Calle-Gómez, Francisco Javier
Isasi, Pedro
Publisher: Springer
Issued date: Jun-2012
Citation: Applied Intelligence, June 2012, volume 36, number 4, pp. 899-917
URI: http://hdl.handle.net/10016/14450
ISSN: 0924-669X
DOI: http://dx.doi.org/10.1007/s10489-011-0304-1
Description: The original publication is available at www.springerlink.com
Abstract: One of the most important types of applications currently being used to share knowledge across the Internet are social networks. In addition to their use in social, professional and organizational spheres, social networks are also frequently utilized by researchers in the social sciences, particularly in anthropology and social psychology. In order to obtain information related to a particular social network, analytical techniques are employed to represent the network as a graph, where each node is a distinct member of the network and each edge is a particular type of relationship between members including, for example, kinship or friendship. This article presents a proposal for the efficien solution to one of the most frequently requested services on social networks; namely, taking different types of relationships into account in order to locate a particular member of the network. The solution is based on a biologically-inspired modificatio of the ant colony optimization algorithm.
Sponsor: This study was funded through a competitive grant awarded by the Spanish Ministry of Education and Science for the THUBAN Project (TIN2008-02711) and through MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government.
Publisher version: http://dx.doi.org/10.1007/s10489-011-0304-1
Keywords: Large graphs
Social networks
ACO
Dijkstra
Path search
Rights: © Springer
Appears in Collections:DI - LABDA - Artículos de Revistas
DI - GCERN - Artículos de revistas científicas

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