The art of community detection
The art of community detection
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DOI：
10.1002/bies.20820
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发表时间：
2008-10-01
期刊：
影响因子：
4
通讯作者：
Lehmann, Sune
中科院分区：
文献类型：
--
作者：
Gulbahce, Natali;Lehmann, Sune
自然界中的网络具有大量的结构。通过一系列数据驱动的发现，网络科学的前沿最近已经从假设数学图论的随机图可以准确地描述真实的网络发展到目前的观点，即网络本质上是高度复杂和结构化的实体。识别网络中的高阶结构揭示了对其功能组织的见解。最近，Clauset、Moore和Newman(1)引入了一种新的算法，该算法通过利用必然组织多层结构的层次结构来识别复杂网络中的这种异质性。在这里，我们将他们的算法固定在一个通用的社区检测框架中，并讨论社区检测的未来。生物学报（英文版），2008。(C) 2008 Wiley期刊有限公司
Networks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman,((1)) introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection. BioEssays 30:934-938, 2008. (C) 2008 Wiley Periodicals, Inc.