Mixed membership distribution-free model
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Abstract (arXiv)
We consider the problem of community detection in overlapping weighted networks, where nodes can belong to multiple communities and edge weights can be finite real numbers. To model such complex networks, we propose a general framework - the mixed membership distribution-free (MMDF) model. MMDF has no distribution constraints of edge weights and can be viewed as generalizations of some previous models, including the well-known mixed membership stochastic blockmodels. Especially, overlapping signed networks with latent community structures can also be generated from our model. We use an efficient spectral algorithm with a theoretical guarantee of convergence rate to estimate community memberships under the model. We also propose the fuzzy weighted modularity to evaluate the quality of community detection for overlapping weighted networks with positive and negative edge weights. We then provide a method to determine the number of communities for weighted networks by taking advantage of our fuzzy weighted modularity. Numerical simulations and real data applications are carried out to demonstrate the usefulness of our mixed membership distribution-free model and our fuzzy weighted modularity.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 30 of its 98 references that are in the map. First posted on arXiv 4 Dec 2021.
Builds on
30 of 98 references.
References · 30
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18 more
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- 2021Degree-corrected distribution-free model for community detection in weighted networkscited by 2
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.