模块化(生物学)
计算机科学
集合(抽象数据类型)
数据科学
光学(聚焦)
最大化
比例(比率)
群落结构
复杂网络
人工智能
万维网
生物
地理
心理学
地图学
光学
物理
社会心理学
程序设计语言
遗传学
生态学
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2023-01-01
卷期号:: 149-171
被引量:2
标识
DOI:10.1016/b978-0-323-85280-7.00016-6
摘要
Many real-world networks, including nervous systems, exhibit meso-scale structure. This means that their elements can be grouped into meaningful subnetworks. In general, these subnetworks are unknown ahead of time and must be “discovered” algorithmically using community detection methods. In this chapter, we review evidence that nervous systems exhibit meso-scale structure in the form of communities, clusters, and modules. We also provide a set of guidelines to assist users in applying community detection methods to their own network data. These guidelines focus on the method of modularity maximization but, in many cases, are general and applicable to other techniques.
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