Overlapping Community Detection via Adaptive Bayesian Nonnegative Matrix Tri-factorization

With the increasing complexity of networks across various domains driven by the advancement of informatization, community detection is no longer confined to disjoint communities but also extends to overlapping communities. Nowadays, efficient algorithms for overlapping community detection (OCD) are highly sought after. Classical Bayesian theory-based nonnegative matrix factorization methods for OCD have laid a solid foundation, but they still have limitations in adaptability to networks with varying overlap degrees. This article presents an adaptive Bayesian nonnegative matrix tri-factorization (ABNMTF) model for OCD. This model can adjust both the node-community interaction strength and the community influence, providing strong adaptability for detecting overlapping communities in networks with varying degrees of overlap. Moreover, iterative and efficient matrix updating rules are introduced to solve the ABNMTF probabilistic model. The superiority of ABNMTF lies in its intuitive architecture, efficient computation, high-precision soft classification, and stable detection results. Experimental results, from both real-world and Lancichinetti-Fortunato-Radicchi (LFR) synthetic networks, illustrate that ABNMTF generally outperforms eleven state-of-the-art OCD algorithms, thereby highlighting its powerful capabilities and stability in detecting overlapping communities.