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Nonparametric Bayesian inference of the microcanonical stochastic block model
Tiago P. Peixoto
A principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is c…
Model selection and hypothesis testing for large-scale network models with overlapping groups
Tiago P. Peixoto
The effort to understand network systems in increasing detail has resulted in a diversity of methods designed to extract their large-scale structure from data. Unfortunately, many…
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
Tiago P. Peixoto
We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method,…
Hierarchical Block Structures and High-resolution Model Selection in Large Networks
Tiago P. Peixoto
Discovering and characterizing the large-scale topological features in empirical networks are crucial steps in understanding how complex systems function. However, most existing me…
Eigenvalue Spectra of Modular Networks
Tiago P. Peixoto
A large variety of dynamical processes that take place on networks can be expressed in terms of the spectral properties of some linear operator which reflects how the dynamical rul…
Parsimonious module inference in large networks
Tiago P. Peixoto
We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length (MDL) principle which see…