  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.
