  Affinity propagation is one of the most effective unsupervised pattern
recognition algorithms for data clustering in high-dimensional feature space.
However, the numerous attempts to test its performance for community detection
in complex networks have been attaining results very far from the state of the
art methods such as Infomap and Louvain. Yet, all these studies agreed that the
crucial problem is to convert the unweighted network topology in a
'smart-enough' node dissimilarity matrix that is able to properly address the
message passing procedure behind affinity propagation clustering. Here we
introduce a conceptual innovation and we discuss how to leverage network latent
geometry notions in order to design dissimilarity matrices for affinity
propagation community detection. Our results demonstrate that the latent
geometry inspired dissimilarity measures we design bring affinity propagation
to equal or outperform current state of the art methods for community
detection. These findings are solidly proven considering both synthetic
'realistic' networks (with known ground-truth communities) and real networks
(with community metadata), even when the data structure is corrupted by noise
artificially induced by missing or spurious connectivity.
