In this short note, we address the identifiability issues inherent in the Degree-Corrected Stochastic Block Model (DCSBM). We provide a rigorous proof demonstrating that the parameters of the DCSBM are identifiable up to a scaling factor and a permutation of the community labels, under a mild condition.

翻译：在这篇短文中，我们探讨了度修正随机块模型（DCSBM）中固有的可识别性问题。我们给出了严格证明，表明在温和条件下，DCSBM的参数在尺度因子和社区标签置换的意义下是可识别的。