A Community Detecting Algorithm Based on Granular Computing

被引：0

作者：

Liu, Lu ^{[1
]}

Qiu, Taorong ^{[1
]}

Bai, Xiaoming ^{[1
]}

Lin, Zhongda ^{[1
]}

机构：

[1] Nanchang Univ, Dept Comp, Nanchang 330031, Peoples R China

来源：

ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014 | 2014年 / 8818卷

关键词：

Social network; Community Detecting; Similarity; Granular Computing; Rough Set; COMPLEX NETWORKS;

D O I：

10.1007/978-3-319-11740-9_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Detecting the community structure of social network is really a very challenging and promising research in the world today. Granular Computing, which can simplify the solution of problem by generating granules and implementation in different granularity spaces, is a kind of intelligent information processing model to simulate the human thinking. In this paper, a model of mining community structure based on granular computing is proposed through improving the similarity between nodes, that is, to design a corresponding mining algorithm by decomposing the problem in different granularity spaces so as to realize the structure detecting. The experimental results on three classic data sets show that the mining algorithm presented in this paper is reasonable.

引用

页码：272 / 284

页数：13

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