A literature survey that proposes a multidimensional taxonomy for community detection, introduces a general mathematical formalization accommodating disjoint/overlapping/fuzzy structures, reviews modularity functions and both algorithmic and mathematical programming methods, and discusses benchmark
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
ALCMeans combines Laplacian energy-based automatic center identification with DeepWalk embeddings to perform unsupervised community detection without predefining the number of communities and reports 10-20% higher NMI and ARI than several baselines on benchmark datasets.
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A Survey of Community Detection from an Operations Research Perspective: Taxonomy, Mathematical Formulations, Modularity Functions, and Benchmark Datasets
A literature survey that proposes a multidimensional taxonomy for community detection, introduces a general mathematical formalization accommodating disjoint/overlapping/fuzzy structures, reviews modularity functions and both algorithmic and mathematical programming methods, and discusses benchmark
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Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
ALCMeans combines Laplacian energy-based automatic center identification with DeepWalk embeddings to perform unsupervised community detection without predefining the number of communities and reports 10-20% higher NMI and ARI than several baselines on benchmark datasets.