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