[图书][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
Modeling heterogeneity in random graphs through latent space models: a selective review
Modeling heterogeneity in random graphs through latent space models: a selective review\*
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …
Hierarchical block structures and high-resolution model selection in large networks
TP Peixoto - Physical Review X, 2014 - APS
Discovering and characterizing the large-scale topological features in empirical networks
are crucial steps in understanding how complex systems function. However, most existing …
are crucial steps in understanding how complex systems function. However, most existing …
Pseudo-likelihood methods for community detection in large sparse networks
Pseudo-likelihood methods for community detection in large sparse networks Page 1 The
Annals of Statistics 2013, Vol. 41, No. 4, 2097–2122 DOI: 10.1214/13-AOS1138 © Institute of …
Annals of Statistics 2013, Vol. 41, No. 4, 2097–2122 DOI: 10.1214/13-AOS1138 © Institute of …
[HTML][HTML] Exponential-family random graph models for valued networks
PN Krivitsky - Electronic journal of statistics, 2012 - ncbi.nlm.nih.gov
Exponential-family random graph models (ERGMs) provide a principled and flexible way to
model and simulate features common in social networks, such as propensities for …
model and simulate features common in social networks, such as propensities for …
How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience
Species are linked to each other by a myriad of positive and negative interactions. This
complex spectrum of interactions constitutes a network of links that mediates ecological …
complex spectrum of interactions constitutes a network of links that mediates ecological …
Stochastic blockmodels with a growing number of classes
We present asymptotic and finite-sample results on the use of stochastic blockmodels for the
analysis of network data. We show that the fraction of misclassified network nodes …
analysis of network data. We show that the fraction of misclassified network nodes …
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
TP Peixoto - Physical Review E, 2014 - APS
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) …
networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) …
Consistency of maximum-likelihood and variational estimators in the stochastic block model
The stochastic block model (SBM) is a probabilistic model designed to describe
heterogeneous directed and undirected graphs. In this paper, we address the asymptotic …
heterogeneous directed and undirected graphs. In this paper, we address the asymptotic …
Inferring the mesoscale structure of layered, edge-valued, and time-varying networks
TP Peixoto - Physical Review E, 2015 - APS
Many network systems are composed of interdependent but distinct types of interactions,
which cannot be fully understood in isolation. These different types of interactions are often …
which cannot be fully understood in isolation. These different types of interactions are often …