Degree-corrected stochastic block models and reliability in networks

Plenty of algorithms for link prediction have been proposed to extract missing information, identify spurious interactions, reconstruct networks, and so on. Stochastic block models are one of the most accurate methods among all of them. However, this algorithm is designed only for simple graphs and ignores the variation in node degree which is typically displayed in real-world networks. In this paper, we propose a corresponding reliable approach based on degree-corrected stochastic block models, which could be applied in networks containing both multi-edges and self-edges. Empirical comparison on five disparate networks shows that the overall performance of our method is better than the original version in predicting missing links, especially for the interactions between high-degree nodes.