Mixed membership distribution-free model

TL;DR

This paper introduces the MMDF model for community detection in complex overlapping weighted networks, allowing for flexible edge weights and providing an efficient spectral algorithm with theoretical guarantees.

Contribution

It proposes the first distribution-free mixed membership model for weighted networks, along with a spectral algorithm and fuzzy modularity for community detection and evaluation.

Findings

Spectral algorithm converges with theoretical guarantees.

Fuzzy weighted modularity effectively evaluates community quality.

Method accurately detects communities in real and simulated data.

Abstract

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.