TY - UNPB
T1 - Bayesian stochastic blockmodeling
AU - Peixoto, Tiago P.
N1 - 42 pages, 16 figures, Chapter in "Advances in Network Clustering and Blockmodeling", edited by P. Doreian, V. Batagelj, A. Ferligoj, (Wiley, New York, 2018 [forthcoming])
PY - 2017/5/29
Y1 - 2017/5/29
N2 - This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic block model (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects on the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.
AB - This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic block model (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects on the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.
KW - stat.ML
KW - cond-mat.stat-mech
KW - physics.data-an
UR - https://arxiv.org/abs/1705.10225v1
UR - https://arxiv.org/abs/1705.10225v1
M3 - Working paper
BT - Bayesian stochastic blockmodeling
PB - arXiv
ER -