Graph embedding methods are becoming increasingly popular in the machine learning community, where they are widely used for tasks such as node classification and link prediction. Embedding graphs in geometric spaces should aid the identification of network communities as well, because nodes in the same community should be projected close to each other in the geometric space, where they can be detected via standard data clustering algorithms. In this paper, we test the ability of several graph embedding techniques to detect communities on benchmark graphs. We compare their performance against that of traditional community detection algorithms. We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. However, the optimal parameter set varies with the specific features of the benchmark graphs, like their size, whereas popular community detection algorithms do not require any parameter. So it is not possible to indicate beforehand good parameter sets for the analysis of real networks. This finding, along with the high computational cost of embedding a network and grouping the points, suggests that, for community detection, current embedding techniques do not represent an improvement over network clustering algorithms.

中文翻译：

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使用图嵌入的网络社区检测

图嵌入方法在机器学习社区中变得越来越流行，它们被广泛用于节点分类和链接预测等任务。在几何空间中嵌入图也应该有助于识别网络社区，因为同一社区中的节点应该在几何空间中彼此靠近投影，在那里它们可以通过标准数据聚类算法进行检测。在本文中，我们测试了几种图嵌入技术在基准图上检测社区的能力。我们将它们的性能与传统社区检测算法的性能进行了比较。We find that the performance is comparable, if the parameters of the embedding techniques are suitably chosen. 然而，最优参数集随着基准图的特定特征而变化，喜欢它们的大小，而流行的社区检测算法不需要任何参数。因此，不可能事先指出用于分析真实网络的良好参数集。这一发现以及嵌入网络和分组点的高计算成本表明，对于社区检测，当前的嵌入技术并不代表对网络聚类算法的改进。