The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, pour évaluer systématiquement les méthodes d'estimation des caractéristiques des nœuds, nous proposons d'abord un modèle de génération de réseaux sociaux appelé LRE (Linkage with Relative Evaluation). LRE est un modèle de génération de réseau, qui vise à reproduire les caractéristiques d'un réseau social. LRE utilise le fait que les gens construisent généralement des relations avec les autres sur la base d’une évaluation relative plutôt que d’une évaluation absolue. Nous évaluons ensuite de manière approfondie la précision de la méthode d'estimation appelée SSI (Structural Superiority Index). Nous révélons que SSI est efficace pour trouver de bons nœuds (par exemple, les 10 % de nœuds supérieurs), mais ne peut pas être utilisé pour trouver d'excellents nœuds (par exemple, les 1 % de nœuds supérieurs). Pour atténuer les problèmes de SSI, nous proposons un nouveau schéma visant à améliorer les méthodes d'estimation existantes appelé RENC (Recursive Estimation of Node Characteristic). RENC réduit l'effet du bruit en estimant de manière récursive les caractéristiques des nœuds. En étudiant la précision de l'estimation avec RENC, nous montrons que RENC est très efficace pour améliorer la précision de l'estimation dans des situations pratiques.
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Kouhei SUGIYAMA, Hiroyuki OHSAKI, Makoto IMASE, "Estimating Node Characteristics from Topological Structure of Social Networks" in IEICE TRANSACTIONS on Communications,
vol. E92-B, no. 10, pp. 3094-3101, October 2009, doi: 10.1587/transcom.E92.B.3094.
Abstract: In this paper, for systematically evaluating estimation methods of node characteristics, we first propose a social network generation model called LRE (Linkage with Relative Evaluation). LRE is a network generation model, which aims to reproduce the characteristics of a social network. LRE utilizes the fact that people generally build relationships with others based on relative evaluation, rather than absolute evaluation. We then extensively evaluate the accuracy of the estimation method called SSI (Structural Superiority Index). We reveal that SSI is effective for finding good nodes (e.g., top 10% nodes), but cannot be used for finding excellent nodes (e.g., top 1% nodes). For alleviating the problems of SSI, we propose a novel scheme for enhancing existing estimation methods called RENC (Recursive Estimation of Node Characteristic). RENC reduces the effect of noise by recursively estimating node characteristics. By investigating the estimation accuracy with RENC, we show that RENC is quite effective for improving the estimation accuracy in practical situations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.3094/_p
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@ARTICLE{e92-b_10_3094,
author={Kouhei SUGIYAMA, Hiroyuki OHSAKI, Makoto IMASE, },
journal={IEICE TRANSACTIONS on Communications},
title={Estimating Node Characteristics from Topological Structure of Social Networks},
year={2009},
volume={E92-B},
number={10},
pages={3094-3101},
abstract={In this paper, for systematically evaluating estimation methods of node characteristics, we first propose a social network generation model called LRE (Linkage with Relative Evaluation). LRE is a network generation model, which aims to reproduce the characteristics of a social network. LRE utilizes the fact that people generally build relationships with others based on relative evaluation, rather than absolute evaluation. We then extensively evaluate the accuracy of the estimation method called SSI (Structural Superiority Index). We reveal that SSI is effective for finding good nodes (e.g., top 10% nodes), but cannot be used for finding excellent nodes (e.g., top 1% nodes). For alleviating the problems of SSI, we propose a novel scheme for enhancing existing estimation methods called RENC (Recursive Estimation of Node Characteristic). RENC reduces the effect of noise by recursively estimating node characteristics. By investigating the estimation accuracy with RENC, we show that RENC is quite effective for improving the estimation accuracy in practical situations.},
keywords={},
doi={10.1587/transcom.E92.B.3094},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Estimating Node Characteristics from Topological Structure of Social Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 3094
EP - 3101
AU - Kouhei SUGIYAMA
AU - Hiroyuki OHSAKI
AU - Makoto IMASE
PY - 2009
DO - 10.1587/transcom.E92.B.3094
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2009
AB - In this paper, for systematically evaluating estimation methods of node characteristics, we first propose a social network generation model called LRE (Linkage with Relative Evaluation). LRE is a network generation model, which aims to reproduce the characteristics of a social network. LRE utilizes the fact that people generally build relationships with others based on relative evaluation, rather than absolute evaluation. We then extensively evaluate the accuracy of the estimation method called SSI (Structural Superiority Index). We reveal that SSI is effective for finding good nodes (e.g., top 10% nodes), but cannot be used for finding excellent nodes (e.g., top 1% nodes). For alleviating the problems of SSI, we propose a novel scheme for enhancing existing estimation methods called RENC (Recursive Estimation of Node Characteristic). RENC reduces the effect of noise by recursively estimating node characteristics. By investigating the estimation accuracy with RENC, we show that RENC is quite effective for improving the estimation accuracy in practical situations.
ER -