2020
Camacho, David; Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Gonzalez-Pardo, Antonio; Cambria, Erik
The four dimensions of social network analysis: An overview of research methods, applications, and software tools Journal Article
In: Information Fusion, vol. 63, pp. 88–120, 2020.
Links | BibTeX | Tags: Big Data, Data Science, Social Networks, Social Networks Analysis, Visualization methods
@article{camacho2020four,
title = {The four dimensions of social network analysis: An overview of research methods, applications, and software tools},
author = {David Camacho and Ángel Panizo-LLedot and Gema Bello-Orgaz and Antonio Gonzalez-Pardo and Erik Cambria},
doi = {10.1016/j.inffus.2020.05.009},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Information Fusion},
volume = {63},
pages = {88--120},
publisher = {Elsevier},
keywords = {Big Data, Data Science, Social Networks, Social Networks Analysis, Visualization methods},
pubstate = {published},
tppubtype = {article}
}
2011
Delgado, Maria Soledad; Gonzalo, Consuelo; Martinez, Estibaliz; Arquero, Agueda
A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps Journal Article
In: Neurocomputing, vol. 74, no. 16, pp. 2624-2632, 2011, ISSN: 0925-2312, (Advances in Extreme Learning Machine: Theory and Applications Biological Inspired Systems. Computational and Ambient Intelligence).
Abstract | Links | BibTeX | Tags: Delaunay triangulation, Growing cell structures, Self-organizing map, Topology preserving, Visualization methods
@article{DELGADO20112624,
title = {A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps},
author = {Maria Soledad Delgado and Consuelo Gonzalo and Estibaliz Martinez and Agueda Arquero},
url = {https://www.sciencedirect.com/science/article/pii/S0925231211002396},
doi = {https://doi.org/10.1016/j.neucom.2011.03.021},
issn = {0925-2312},
year = {2011},
date = {2011-01-01},
journal = {Neurocomputing},
volume = {74},
number = {16},
pages = {2624-2632},
abstract = {The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, particularly using Kohonen's model. In this work, two methods for measuring the topology preservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map.},
note = {Advances in Extreme Learning Machine: Theory and Applications Biological Inspired Systems. Computational and Ambient Intelligence},
keywords = {Delaunay triangulation, Growing cell structures, Self-organizing map, Topology preserving, Visualization methods},
pubstate = {published},
tppubtype = {article}
}
The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, particularly using Kohonen's model. In this work, two methods for measuring the topology preservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map.