Detecting communities in large networks has become a common practice in socio-spatial analyses and has led to the development of numerous dedicated mathematical algorithms. Nowadays, however, researchers face a deluge of data and algorithms, and great care must be taken regarding methodological questions such as the values of the parameters and the geographical characteristics of the data. We aim here at testing the sensitivity of multi-scale modularity optimized by the Louvain method to the value of the resolution parameter (introduced by Reichardt and Bornholdt (Phys Rev Lett 93(21):218701, 2004. https://doi.org/10.1103/PhysRevLett.93.218701) and controlling the size of the communities) and to a number of spatial issues such as the inclusion of internal loops and the delineation of the study area. We compare the community structures with those found by another well-known community detection algorithm (Infomap), and we further interpret the final results in terms of urban geography. Sensitivity analyses are conducted for commuting movements in and around Brussels. Results reveal slight effects of spatial issues (inclusion of the internal loops, definition of the study area) on the partition into job basins, while the resolution parameter plays a major role in the final results and their interpretation in terms of urban geography. Community detection methods seem to reveal a surprisingly strong spatial effect of commuting patterns: Similar partitions are obtained with different methods. This paper highlights the advantages and sensitivities of the multi-scale Louvain method and more particularly of defining communities of places. Despite these sensitivities, the method proves to be a valuable tool for geographers and planners.

中文翻译：

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使用多尺度Louvain方法检测社区：布鲁塞尔都会区的鲁棒性测试

在大型网络中检测社区已成为社会空间分析中的一种普遍做法，并导致了众多专用数学算法的发展。但是，如今，研究人员面临着大量的数据和算法，必须特别注意方法论问题，例如参数的值和数据的地理特征。我们的目标是测试通过Louvain方法优化的多尺度模块对分辨率参数值的敏感性（由Reichardt和Bornholdt引入（Phys Rev Lett 93（21）：218701，2004.https：//doi.org /10.1103/PhysRevLett.93.218701）和控制社区的规模）以及许多空间问题，例如包含内部环路和研究区域的轮廓。我们将社区结构与另一种著名的社区检测算法（Infomap）所发现的结构进行比较，并根据城市地理学进一步解释最终结果。进行布鲁塞尔及其周围通勤运动的灵敏度分析。结果揭示了空间问题（包括内部循环，研究区域的定义）对划分为工作盆地的影响，而分辨率参数在最终结果及其对城市地理学的解释中起主要作用。社区检测方法似乎显示出惊人的通勤模式强大的空间效应：使用不同的方法可以获得类似的分区。本文重点介绍了多尺度Louvain方法的优势和敏感性，尤其是定义场所社区的优势和敏感性。