Finding community structure using the ordered random graph model

Visualization of the adjacency matrix enables us to capture macroscopic features a network when elements are aligned properly. Community structure, consisting several densely connected components, is particularly important feature and structure can be identified through it close block-diagonal form. However, classical ordering algorithms for matrices fail align such that community visible. In this study, we propose an algorithm based on maximum-likelihood estimate ordered random graph model. We show proposed method allows more clearly identify structures than existing algorithms.