NETWORK COMMUNITY DETECTION BASED ON SPECTRAL CLUSTERING

被引：0

作者：

Qiu, Jing ^{[1
]}

Peng, Jing ^{[1
]}

Zhai, Ying ^{[2
]}

机构：

[1] Hebei Univ Sci & Technol, Dept Informat Sci & Engn, Shijiazhuang 050018, Peoples R China

[2] Hebei Univ Econ & Business, Dept Informat & Technol, Shijiazhuang 050061, Peoples R China

来源：

PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2014年

基金：

中国国家自然科学基金;

关键词：

Laplacian matrix; Spectral vlustering; K-means; Community detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, spectral clustering based on the spectral graph theory has become one of the most popular clustering algorithms. It is easy to implement and is widely used in the domain of pattern recognition. In this paper, a new method is proposed to estimate the number of communities based on spectral clustering. The conductivity function and the accuracy are used to evaluate the quality of community detection. Experimental results on Zachary Karate Club show that the proposed method yields a high accuracy and effectiveness.

引用

页码：648 / 652

页数：5

共 9 条

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