(Open access funding provided by Semmelweis University)
Graph embeddings learn the structure of networks and represent it in low-dimensional
vector spaces. Community structure is one of the features that are recognized and
reproduced by embeddings. We show that an iterative procedure, in which a graph is
repeatedly embedded and its links are reweighted based on the geometric proximity
between the nodes, reinforces intra-community links and weakens inter-community links,
making the clusters of the initial network more visible and more easily detectable.
The geometric separation between the communities can become so strong that even a
very simple parsing of the links may recover the communities as isolated components
with surprisingly high precision. Furthermore, when used as a pre-processing step,
our embedding and reweighting procedure can improve the performance of traditional
community detection algorithms.
