Identification of network modules by optimization of ratio association

被引：52

作者：

Angelini, L. ^{[1
]}

Boccaletti, S.

Marinazzo, D.

Pellicoro, M.

Stramaglia, S.

机构：

[1] Univ Bari, TIRES, Ctr Innovat Technol Signal Detect & Proc, I-70126 Bari, Italy

[2] Dipartimento Interateneo Fis, I-70126 Bari, Italy

[3] Ist Nazl Fis Nucl, Sez Bari, I-70126 Bari, Italy

[4] CNR, Ist Sist Complessi, I-50019 Sesto Fiorentino, Italy

[5] Italian Embassy Tel Aviv, IL-68125 Tel Aviv, Israel

来源：

CHAOS | 2007年 / 17卷 / 02期

关键词：

D O I：

10.1063/1.2732162

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We introduce a novel method for identifying the modular structures of a network based on the maximization of an objective function: the ratio association. This cost function arises when the communities detection problem is described in the probabilistic autoencoder frame. An analogy with kernel k-means methods allows us to develop an efficient optimization algorithm, based on the deterministic annealing scheme. The performance of the proposed method is shown on real data sets and on simulated networks. (c) 2007 American Institute of Physics.

引用

页数：6

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