A nonparametric view of network models and Newman-Girvan and other modularities

被引：453

作者：

Bickel, Peter J. ^{[1
]}

Chen, Aiyou ^{[2
]}

机构：

[1] Univ Calif Berkeley, Berkeley, CA 94720 USA

[2] Alcatel Lucent Bell Labs, Murray Hill, NJ 07974 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2009年 / 106卷 / 50期

关键词：

modularity; profile likelihood; ergodic model; spectral clustering; STOCHASTIC BLOCKMODELS;

D O I：

10.1073/pnas.0907096106

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Prompted by the increasing interest in networks in many fields, we present an attempt at unifying points of view and analyses of these objects coming from the social sciences, statistics, probability and physics communities. We apply our approach to the Newman-Girvan modularity, widely used for "community" detection, among others. Our analysis is asymptotic but we show by simulation and application to real examples that the theory is a reasonable guide to practice.

引用

页码：21068 / 21073

页数：6

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