Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering
This work addresses the problem of identifying communities in various real-world networks, such as social and transportation graphs, offering a hierarchical approach that is useful when the number of correct partitions is unknown, though it is incremental in nature.
The paper tackles community detection in complex networks by proposing three hierarchical frameworks that combine node similarity, graph representation learning, and hierarchical clustering, achieving competitive performance with state-of-the-art algorithms like Leiden and Louvain while providing nested partitions for flexibility.
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.