Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Pavel N. Krivitsky"'
Publikováno v:
Journal of Statistical Software, Vol 105, Pp 1-44 (2023)
The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an o
Externí odkaz:
https://doaj.org/article/9d0262b9890244269847ed0f315e4d88
Publikováno v:
IEEE Access, Vol 10, Pp 40482-40495 (2022)
Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust unce
Externí odkaz:
https://doaj.org/article/d4d1d86a01f14f34bc658af53bca7e87
Publikováno v:
IEEE Access, Vol 9, Pp 130353-130365 (2021)
Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data. More recently, there has been more attention to unstructured data that can be represented via
Externí odkaz:
https://doaj.org/article/5a3e1b8cd08e435db246890db55e6705
Autor:
Pavel N. Krivitsky, Mark S. Handcock
Publikováno v:
Journal of Statistical Software, Vol 24, Iss 5 (2007)
latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested an approach to modeling networks based on positing the existence of an latent space of characteristi
Externí odkaz:
https://doaj.org/article/67553015fd1c4e82a5212f1f303199c7
Publikováno v:
International Journal of Finance & Economics. 27:554-570
Publikováno v:
Soc Networks
Egocentric sampling of networks selects a subset of nodes (“egos”) and collects information from them on themselves and their immediate network neighbours (“alters”), leaving the rest of the nodes in the network unobserved. This design is pop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed4bbf0d06e621c65572305dab0c052d
https://europepmc.org/articles/PMC8993043/
https://europepmc.org/articles/PMC8993043/
Publikováno v:
Statist. Sci. 35, no. 4 (2020), 627-662
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling dense and sparse random graphs with short- or long-tailed degree distributions, covariate effects and a wide range of complex dependencies. Special c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7480295cd785aed5eafc9ceafe863bb
https://projecteuclid.org/euclid.ss/1605603638
https://projecteuclid.org/euclid.ss/1605603638
Publikováno v:
Psychometrika
Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5360011d9c81f6b79ef2cb13ddea72f5
https://europepmc.org/articles/PMC9478997/
https://europepmc.org/articles/PMC9478997/
Autor:
Pavel N. Krivitsky
Publikováno v:
Computational Statistics & Data Analysis. 107:149-161
Exponential-family models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMC-based techniques to fit, the latter requiring an
Publikováno v:
Journal of the Royal Statistical Society Series C: Applied Statistics. 66:481-500
Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the s