模块化(生物学)
分拆(数论)
理论(学习稳定性)
计算机科学
拉普拉斯算子
网络分区
度量(数据仓库)
图形
空模式
群落结构
模块化设计
复杂网络
理论计算机科学
拉普拉斯矩阵
限制
桥接(联网)
数学
数据挖掘
分布式计算
机器学习
组合数学
数学分析
工程类
操作系统
生物
万维网
机械工程
遗传学
计算机网络
作者
Renaud Lambiotte,Jean-Charles Delvenne,M. Barahona
出处
期刊:Cornell University - arXiv
日期:2008-12-09
被引量:91
摘要
Most methods proposed to uncover communities in complex networks rely on their structural properties. Here we introduce the stability of a network partition, a measure of its quality defined in terms of the statistical properties of a dy namical process taking place on the graph. The time-scale of the process acts as an intrinsic parameter that uncovers community structures at different resolutions. The stability extends and unifies standard notions for community detection: modularity and spectral partitioning can be seen as limiting cases of our dynamic measure. Similarly, recently proposed multi-resolution methods correspond to linearisations of the stability at short times. The connection between community detection and Laplacian dynamics enables us to establish dynamically motivated stability measures linked to distinct null models. We apply our method to find multi-scale partitions for different networks and show that the stability can be computed
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