Noise tolerant community detection using a mixed graph model

被引：0
|
作者：
Keszler A. [1 ]
Kiss A. [1 ]
Sziranyi T. [1 ]
机构：
[1] Computer and Automation Research Institute, MTA SZTAKI, Budapest
来源：
Advances in Intelligent and Soft Computing | 2010年 / 80卷
基金：
匈牙利科学研究基金会;
关键词：
D O I：
10.1007/978-3-642-14989-4_6
中图分类号：
学科分类号：
摘要：
In this paper a new concept is proposed for finding communities in a social network based on a mixed graph theoretic model of a standard and a bipartite graph. Compared to previous methods the introduced algorithm has the advantage of noise-tolerance and is applicable independently of the size of the clusters in the graph. The cluster core-mining method is based on a modified MST algorithm. Clustering incomplete data is done by using bipartite graphs and fuzzy membership functions. © 2010 Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：59 / 68
页数：9
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