Multi-scale community detection using stability optimisation
Multi-scale community detection using stability optimisation
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DOI：
10.1504/ijwbc.2013.054907
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发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
Erwan Le Martelot;C. Hankin
中科院分区：
文献类型：
--
作者：
Erwan Le Martelot;C. Hankin
许多真实的系统可以表示为网络，其分析可以提供关于原始系统的组织的信息。在过去的十年中，社区检测得到了很多关注，现在是一个非常活跃的研究领域。最近，引入了稳定性作为划分质量的新度量。这项工作研究的稳定性作为一个优化标准，利用马尔可夫过程的网络，使多尺度社区检测。提出了优化稳定性的算法的几种算法和变化，以及重叠社区的应用。实验表明，该方法能够实现多尺度网络分析的准确性。
Many real systems can be represented as networks whose analysis can be very informative regarding the original system's organisation. In the past decade, community detection received a lot of attention and is now a very active field of research. Recently, stability was introduced as a new measure for partition quality. This work investigates stability as an optimisation criterion that exploits a Markov process view of networks to enable multi-scale community detection. Several heuristics and variations of an algorithm optimising stability are presented as well as an application to overlapping communities. Experiments show that the method enables accurate multi-scale network analysis.