Mixed membership distribution-free model
This work addresses the problem of analyzing complex networks with overlapping communities and weighted edges for researchers in network science, though it is incremental as it builds on existing mixed membership models.
The authors tackled community detection in overlapping weighted networks by proposing the mixed membership distribution-free (MMDF) model, which generalizes previous models and handles edge weights without distribution constraints, and they developed an efficient spectral algorithm with theoretical guarantees and a fuzzy weighted modularity for evaluation.
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.