论文标题
网络神经科学中的社区发现
Community detection in network neuroscience
论文作者
论文摘要
包括神经系统在内的许多现实世界网络都展示了中尺度结构。这意味着他们的元素可以分为有意义的子网络。通常，这些子网络提前未知，必须使用社区检测方法“发现”算法。在本文中，我们回顾了神经系统以社区，集群和模块的形式表现出中级结构的证据。我们还提供了一组准则，以帮助用户将社区检测方法应用于其自己的网络数据。这些指南专注于模块化最大化方法，但在许多情况下，这些指南均适用于其他技术。
Many real-world networks, including nervous systems, exhibit meso-scale structure. This means that their elements can be grouped into meaningful sub-networks. In general, these sub-networks are unknown ahead of time and must be "discovered" algorithmically using community detection methods. In this article, 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.
