[Submitted on 21 Mar 2023 (v1), last revised 3 Nov 2026 (this version, v3)]
Title:Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering
View a PDF of the paper titled Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering, by {\L}ukasz Brzozowski and 2 other authors
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Abstract:Community detection is a significant challenge in analysing real graphs, including social, citation, biological, 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 364 module combinations on 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 with 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, and we show that it is obtained at near-zero practical cost in terms of partition quality. On graphs composed of many small communities, our pipelines are more accurate than the reference solutions. Nevertheless, the downside of our approach is its time complexity, which confines the framework to networks of some 10000 vertices on personal machines.
Subjects:
Social and Information Networks (cs.SI); Machine Learning (cs.LG)
Cite as: arXiv:2303.12212 [cs.SI]
(or arXiv:2303.12212v3 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2303.12212
arXiv-issued DOI via DataCite
Journal reference: Information Systems 144, 102824, 2027
Related DOI:
https://doi.org/10.1016/j.is.2026.102824
DOI(s) linking to related resources
Submission history
From: Marek Gagolewski [view email] [v1] Tue, 21 Mar 2023 22:12:53 UTC (880 KB)
[v2] Wed, 24 May 2023 02:55:38 UTC (309 KB)
[v3] Tue, 3 Nov 2026 16:48:43 UTC (11,384 KB)
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