Hierarchical block structures and high-resolution model selection in large networks
- Tiago P. Peixoto
- Computer ScienceArXiv
- 16 October 2013
A nested generative model is constructed that, through a complete description of the entire network hierarchy at multiple scales, enables the detection of modular structure at levels far beyond those possible with current approaches, and is based on the principle of parsimony.
Nonparametric Bayesian inference of the microcanonical stochastic block model.
- Tiago P. Peixoto
- Computer SciencePhysical Review E
- 9 October 2016
This work presents a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization, and exposes a direct equivalence between this microcanonical approach and alternative derivations based on the canonical SBM.
Parsimonious module inference in large networks.
- Tiago P. Peixoto
- Computer Science, MathematicsPhysical Review Letters
- 19 December 2012
It is obtained that the maximum number of detectable blocks scales as sqrt[N], where N is the number of nodes in the network, for a fixed average degree ⟨k⟩ and the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm.
Bayesian Stochastic Blockmodeling
- Tiago P. Peixoto
- Computer ScienceAdvances in Network Clustering and Blockmodeling
- 29 May 2017
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 blockmodel (SBM), as well as…
The graph-tool python library
- Tiago P. Peixoto
- Computer Science
- 10 September 2014
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
- Tiago P. Peixoto
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 16 October 2013
An efficient algorithm for the inference of stochastic block models in large networks, capable of delivering results which are indistinguishable from the more exact and numerically expensive MCMC method in many artificial and empirical networks, despite being much faster.
Inferring the mesoscale structure of layered, edge-valued, and time-varying networks.
- Tiago P. Peixoto
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 9 April 2015
A robust and principled method by constructing generative models of modular network structure, incorporating layered, attributed and time-varying properties, as well as a nonparametric Bayesian methodology to infer the parameters from data and select the most appropriate model according to statistical evidence is proposed.
The entropy of stochastic blockmodel ensembles
- Tiago P. Peixoto
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 27 December 2011
This paper derives expressions for the entropy of stochastic blockmodel ensembles from several ensemble variants, including the traditional model as well as the newly introduced degree-corrected version, which imposes a degree sequence on the vertices, in addition to the block structure.
A network approach to topic models
- M. Gerlach, Tiago P. Peixoto, E. Altmann
- Computer ScienceScience Advances
- 4 August 2017
A new approach to topic models finds topics through community detection in word-document networks by adapting existing community-detection methods using a stochastic block model with nonparametric priors, and shows how to formally relate methods from community detection and topic modeling, opening the possibility of cross-fertilization between these two fields.
Network Reconstruction and Community Detection from Dynamics
- Tiago P. Peixoto
- Computer Science, MathematicsPhysical Review Letters
- 26 March 2019
It is shown that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities.
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