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spectralGraphTopology  

Learning Graphs from Data via Spectral Constraints
View on CRAN: Click here


Download and install spectralGraphTopology package within the R console
Install from CRAN:
install.packages("spectralGraphTopology")

Install from Github:
library("remotes")
install_github("cran/spectralGraphTopology")

Install by package version:
library("remotes")
install_version("spectralGraphTopology", "0.2.3")



Attach the package and use:
library("spectralGraphTopology")
Maintained by
Ze Vinicius
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-08
Latest Update: 2022-03-14
Description:
In the era of big data and hyperconnectivity, learning high-dimensional structures such as graphs from data has become a prominent task in machine learning and has found applications in many fields such as finance, health care, and networks. 'spectralGraphTopology' is an open source, documented, and well-tested R package for learning graphs from data. It provides implementations of state of the art algorithms such as Combinatorial Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation based on Majorization-Minimization (GLE-MM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph learning has been widely employed for clustering, where specific algorithms are available in the literature. To this end, we provide an implementation of the Constrained Laplacian Rank (CLR) algorithm.
How to cite:
Ze Vinicius (2019). spectralGraphTopology: Learning Graphs from Data via Spectral Constraints. R package version 0.2.3, https://cran.r-project.org/web/packages/spectralGraphTopology. Accessed 04 Jan. 2025.
Previous versions and publish date:
0.1.0 (2019-05-08 12:30), 0.1.1 (2019-06-03 15:10), 0.1.2 (2019-09-22 23:50), 0.2.0 (2019-10-12 10:00), 0.2.2 (2022-03-11 10:30)
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Complete documentation for spectralGraphTopology
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