Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks
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Affinity propagation is one of the most effective algorithms for data clustering in high-dimensional feature space. However the numerous attempts to test its performance for community detection in real 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 network topology in a 'smart-enough' dissimilarity matrix that is able to properly address the message passing procedure behind affinity propagation clustering. Here 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 dissimilarity measures we designed bring affinity propagation to outperform current state of the art methods for community detection, not only on several original real networks, but also when their structure is corrupted by noise artificially induced by missing or spurious connectivity.
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