Two local dissimilarity measures for weighted graphs with application to protein interaction networks

被引：8

作者：

Angelelli, Jean-Baptiste ^{[1
]}

Baudot, Anais ^{[2
]}

Brun, Christine ^{[2
]}

Guenoche, Alain ^{[1
]}

机构：

[1] Univ Mediterranee, CNRS, IML, F-13288 Marseille 9, France

[2] Univ Mediterranee, CNRS, IBDML, F-13288 Marseille 9, France

来源：

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION | 2008年 / 2卷 / 01期

关键词：

Graph distance; Graph partitioning; Heuristic optimisation; Biological networks;

D O I：

10.1007/s11634-008-0018-3

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We extend the Czekanowski-Dice dissimilarity measure, classically used to cluster the vertices of unweighted graphs, to weighted ones. The first proposed formula corresponds to edges weighted by a probability of existence. The second one is adapted to edges weighted by intensity or strength. We show on simulated graphs that the class identification process is improved by computing weighted compared to unweighted edges. Finally, an application to a drosophila protein network illustrates the fact that using these new formulas improves the 'biological accuracy' of partitioning.

引用

页码：3 / 16

页数：14

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