spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

Block coordinate descent estimators to learn k-component, bipartite, and k-component bipartite graphs from data by imposing spectral constraints on the eigenvalues and eigenvectors of the Laplacian and adjacency matrices. Those estimators leverages spectral properties of the graphical models as a prior information, which turn out to play key roles in unsupervised machine learning tasks such as clustering and community detection. This package is based on the paper "A Unified Framework for Structured Graph Learning via Spectral Constraints" by S. Kumar et al (2019) <arXiv:1904.09792>.

Version: 0.1.1
Imports: Rcpp, osqp, MASS, Matrix, progress, rlist
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Suggests: bookdown, knitr, prettydoc, rmarkdown, R.rsp, testthat, patrick, corrplot, igraph, kernlab, pals, clusterSim, viridis, quadprog, matrixcalc, CVXR
Published: 2019-06-03
Author: Ze Vinicius [cre, aut], Daniel P. Palomar [aut]
Maintainer: Ze Vinicius <jvmirca at gmail.com>
BugReports: https://github.com/dppalomar/spectralGraphTopology/issues
License: GPL-3
URL: https://github.com/dppalomar/spectralGraphTopology, https://mirca.github.io/spectralGraphTopology, https://www.danielppalomar.com
NeedsCompilation: yes
Citation: spectralGraphTopology citation info
Materials: README NEWS
CRAN checks: spectralGraphTopology results


Reference manual: spectralGraphTopology.pdf
Vignettes: Learning graphs from data via spectral constraints (pdf)
Learning graphs from data via spectral constraints (html)
Package source: spectralGraphTopology_0.1.1.tar.gz
Windows binaries: r-devel: spectralGraphTopology_0.1.1.zip, r-release: spectralGraphTopology_0.1.1.zip, r-oldrel: spectralGraphTopology_0.1.1.zip
OS X binaries: r-release: not available, r-oldrel: not available
Old sources: spectralGraphTopology archive


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