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
Para compreender as propriedades estruturais e funcionais de redes complexas de grande escala, é crucial extrair eficientemente um conjunto de sub-redes coesas como comunidades. Vários métodos de extração comunitária foram propostos na literatura, incluindo o clássico kmétodo de decomposição -core e, mais recentemente, o kmétodo de extração comunitária baseado em clique. O kO método -core, embora computacionalmente eficiente, muitas vezes não é poderoso o suficiente para descobrir uma estrutura de comunidade detalhada e produz apenas comunidades de granulação grossa e fracamente conectadas. O kO método -clique, por outro lado, pode extrair comunidades refinadas e fortemente conectadas, mas requer uma quantidade substancial de carga computacional para redes complexas de grande escala. Neste artigo, apresentamos uma nova noção de sub-rede chamada k-denso, e propor um algoritmo eficiente para extrair k-comunidades densas. Aplicamos nosso método aos três diferentes tipos de redes montadas a partir de dados reais, ou seja, de trackbacks de blogs, associações de palavras e referências da Wikipédia, e demonstramos que o kO método denso poderia extrair comunidades quase tão eficientemente quanto o k-core, enquanto as qualidades das comunidades extraídas são comparáveis às obtidas pelo kmétodo -clique.
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Kazumi SAITO, Takeshi YAMADA, Kazuhiro KAZAMA, "Extracting Communities from Complex Networks by the k-Dense Method" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 11, pp. 3304-3311, November 2008, doi: 10.1093/ietfec/e91-a.11.3304.
Abstract: To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.11.3304/_p
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@ARTICLE{e91-a_11_3304,
author={Kazumi SAITO, Takeshi YAMADA, Kazuhiro KAZAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Extracting Communities from Complex Networks by the k-Dense Method},
year={2008},
volume={E91-A},
number={11},
pages={3304-3311},
abstract={To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.},
keywords={},
doi={10.1093/ietfec/e91-a.11.3304},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Extracting Communities from Complex Networks by the k-Dense Method
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3304
EP - 3311
AU - Kazumi SAITO
AU - Takeshi YAMADA
AU - Kazuhiro KAZAMA
PY - 2008
DO - 10.1093/ietfec/e91-a.11.3304
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2008
AB - To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.
ER -