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Abstract

DenGraph-HO is an extension of the density-based graph clustering algorithm DenGraph. It is able to detect dense groups of nodes in a given graph and produces a hierarchy of clusters, which can be efficiently computed. The generated hierarchy can be used to investigate the structure and the characteristics of social networks. Each hierarchy level provides a different level of detail and can be used as the basis for interactive visual social network analysis. After a short introduction of the original DenGraph algorithm, we present DenGraph-HO and its top-down and bottom-up approaches. We describe the data structures and memory requirements and analyse the run-time complexity. Finally, we apply the DenGraph-HO algorithm to the real-world datasets obtained from the online music platform Last.fm and from the former US company Enron.