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
As redes sociais muitas vezes demonstram uma estrutura comunitária hierárquica com comunidades inseridas em outras. A maioria dos métodos de detecção de comunidades hierárquicas existentes precisam de um ou mais parâmetros ajustáveis para controlar os níveis de resolução, e os dendogramas obtidos, uma árvore que descreve a estrutura hierárquica da comunidade, são extremamente complexos de entender e analisar. No artigo, propomos um método de detecção de comunidade hierárquica sem parâmetros baseado em microcomunidade e árvore geradora mínima. O método proposto primeiro identifica microcomunidades com base na força do link entre vértices adjacentes e, em seguida, constrói árvore geradora mínima ligando sucessivamente essas microcomunidades, uma por uma. A estrutura hierárquica da comunidade das redes sociais pode ser revelada intuitivamente a partir da ordem de fusão dessas microcomunidades. Resultados experimentais em redes sintéticas e do mundo real mostram que nosso método proposto apresenta boa precisão e desempenho eficiente e supera outros métodos de última geração. Além disso, nosso método proposto não requer parâmetros predefinidos, e o dendograma de saída é simples e significativo para compreender e analisar a estrutura hierárquica da comunidade das redes sociais.
Zhixiao WANG
China University of Mining and Technology
Mengnan HOU
China University of Mining and Technology
Guan YUAN
China University of Mining and Technology
Jing HE
China University of Mining and Technology
Jingjing CUI
Baidu Online Network Technology (Beijing) Co., Ltd
Mingjun ZHU
China University of Mining and Technology
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Zhixiao WANG, Mengnan HOU, Guan YUAN, Jing HE, Jingjing CUI, Mingjun ZHU, "Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1773-1783, September 2019, doi: 10.1587/transinf.2018EDP7205.
Abstract: Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7205/_p
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@ARTICLE{e102-d_9_1773,
author={Zhixiao WANG, Mengnan HOU, Guan YUAN, Jing HE, Jingjing CUI, Mingjun ZHU, },
journal={IEICE TRANSACTIONS on Information},
title={Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree},
year={2019},
volume={E102-D},
number={9},
pages={1773-1783},
abstract={Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.},
keywords={},
doi={10.1587/transinf.2018EDP7205},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree
T2 - IEICE TRANSACTIONS on Information
SP - 1773
EP - 1783
AU - Zhixiao WANG
AU - Mengnan HOU
AU - Guan YUAN
AU - Jing HE
AU - Jingjing CUI
AU - Mingjun ZHU
PY - 2019
DO - 10.1587/transinf.2018EDP7205
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.
ER -