Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection
A degree-corrected joint matrix factorization detects shared communities across multilayer networks.
The paper proposes joint nonnegative symmetric matrix trifactorization for community detection in multilayer networks. Constraints keep communities disjoint and shared across layers while each layer retains its own connectivity and degree patterns. An efficient solver is evaluated on graphs from the multilayer degree-corrected stochastic block model (MDCBM). Experiments show reliable detection across regimes where prior methods are limited by structural assumptions.
- Joint nonnegative symmetric trifactorization shares disjoint communities across layers.
- Each layer keeps its own connectivity patterns and node degrees.
- Evaluated using the multilayer degree-corrected stochastic block model.
- Existing methods often fail under restrictive structural assumptions.
From the original article
1 min read at arxiv.orgMultilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying settings, or interactions of different types. The goal of community detection in multilayer networks is to identify groups of nodes exhibiting similar connectivity patterns, which may vary across layers.
By Alexandra Dache · arxiv.org
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