A New Adaptive Robust Modularized Semi-Supervised Community Detection Method Based on Non-negative Matrix Factorization

Abstract

The most extensively used tools for categorizing complicated networks are community detection methods. One of the most common methods for unsupervised and semi-supervised clustering is community detection based on Non-negative Matrix Factorization (NMF). Nonetheless, this approach encounters multiple challenges, including the lack of specificity for the data type and the decreased efficiency when errors occur in each cluster’s knowledge priority. As modularity is the basic and thorough criterion for evaluating and validating performance of community detection methods, this paper proposes a new approach for modularity-based community detection which is similar to symmetric NMF. The provided approach is a semi-supervised adaptive robust community detection model referred to as modularized robust semi-supervised adaptive symmetric NMF (MRASNMF). In this model, the modularity criterion has been successfully combined with the NMF model via a novel multi-view clustering method. Also, the tuning parameter is adjusted iteratively via an adaptive method. MRASNMF makes use of knowledge priority, modularity criterion, reinforcement of non-negative matrix factorization, and has iterative solution, as well. In this regard, the MRASNMF model was evaluated and validated using five real-world networks in comparison to existing semi-supervised community detection approaches. According to the findings of this study, the proposed strategy is most effective for all types of networks.