Community detection with and without prior information

被引：29

作者：

Allahverdyan, A. E. ^{[1
]}

Steeg, G. Ver ^{[2
]}

Galstyan, A. ^{[2
]}

机构：

[1] Yerevan Phys Inst, Yerevan 375036, Armenia

[2] Univ So Calif, Inst Informat Sci, Marina Del Rey, CA 90292 USA

来源：

EPL | 2010年 / 90卷 / 01期

关键词：

MEAN-FIELD THEORY; SPIN-GLASS; LATTICE;

D O I：

10.1209/0295-5075/90/18002

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

We study the problem of graph partitioning, or clustering, in sparse networks with prior information about the clusters. Specifically, we assume that for a fraction rho of the nodes their true cluster assignments are known in advance. This can be understood as a semi-supervised version of clustering, in contrast to unsupervised clustering where the only available information is the graph structure. In the unsupervised case, it is known that there is a threshold of the inter-cluster connectivity beyond which clusters cannot be detected. Here we study the impact of the prior information on the detection threshold, and show that even minute (but generic) values of rho > 0 shift the threshold downwards to its lowest possible value. For weighted graphs we show that a small semi-supervising can be used for a non-trivial definition of communities. Copyright (C) EPLA, 2010

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页数：6

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