Fanton Silvia, Thompson William Hedley
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Department of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden.
Netw Neurosci. 2023 Jun 30;7(2):461-477. doi: 10.1162/netn_a_00313. eCollection 2023.
Visualizations of networks are complex since they are multidimensional and generally convey large amounts of information. The layout of the visualization can communicate either network properties or spatial properties of the network. Generating such figures to effectively convey information and be accurate can be difficult and time-consuming, and it can require expert knowledge. Here, we introduce (short for ), a Python package for Python 3.9+. The package offers several advantages. First, provides a high-level interface to easily highlight and customize results of interest. Second, it presents a solution to promote accurate plots through its integration with . Third, it integrates with other Python software, allowing for easy integration to include networks from or implementations of network-based statistics. In sum, is a versatile but easy to use package designed to produce high-quality network figures while integrating with open research software for neuroimaging and network theory.
网络可视化很复杂,因为它们是多维的,通常会传达大量信息。可视化的布局可以传达网络属性或网络的空间属性。生成这样的图形以有效地传达信息并保证准确性可能既困难又耗时,而且可能需要专业知识。在这里,我们介绍 ( 简称),一个适用于Python 3.9+的Python包。该包具有几个优点。首先, 提供了一个高级接口,可轻松突出显示和自定义感兴趣的结果。其次,它通过与 的集成提供了一种促进精确绘图的解决方案。第三,它与其他Python软件集成,允许轻松集成来自 或基于网络的统计实现的网络。总之, 是一个通用但易于使用的包,旨在生成高质量的网络图形,同时与用于神经成像和网络理论的开放研究软件集成。