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Community detection is a critical challenge in analysing real graphs,including social, transportation, citation, cybersecurity, and many othernetworks. This article proposes three new, general, hierarchical frameworks todeal with this task. The introduced approach supports various linkage-basedclustering algorithms, vertex proximity matrices, and graph representationlearning models. We compare over a hundred module combinations on theStochastic Block Model graphs and real-life datasets. We observe that our bestpipelines (Wasserman-Faust and the mutual information-based PPMI proximity, aswell as the deep learning-based DNGR representations) perform competitively tothe state-of-the-art Leiden and Louvain algorithms. At the same time, unlikethe latter, they remain hierarchical. Thus, they output a series of nestedpartitions of all possible cardinalities which are compatible with each other.This feature is crucial when the number of correct partitions is unknown inadvance.

Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering