Community Detection in Degree-Corrected Block Models

In many fields such as social science, neuroscience and computer science, it has become increasingly important to process and make inference on relational data. The analysis of network data, a prevalent form of relational data, becomes an important topic for statistics and machine learning. One central problem of network data analysis is community detection: to partition the nodes in a network into subsets. A meaningful partition of nodes can often uncover interesting information that is not apparent in a complicated network. An important line of research on community detection is based on Stochastic Block Models (SBMs) [14]. For any p [0, 1], let Bern(p) be the Bernoulli distribution with success probability p.