  Core-periphery structure is an essential mesoscale feature in complex
networks. Previous researches mostly focus on discriminative approaches while
in this work, we propose a generative model called masked Bayesian non-negative
matrix factorization. We build the model using two pair affiliation matrices to
indicate core-periphery pair associations and using a mask matrix to highlight
connections to core nodes. We propose an approach to infer the model
parameters, and prove the convergence of variables with our approach. Besides
the abilities as traditional approaches, it is able to identify core scores
with overlapping core-periphery pairs. We verify the effectiveness of our
method using randomly generated networks and real-world networks. Experimental
results demonstrate that the proposed method outperforms traditional
approaches.
