In the era of big data and hyperconnectivity, learning highdimensional 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 welltested 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 MajorizationMinimization (GLEMM), and Graph Estimation based on Alternating Direction Method of Multipliers (GLEADMM). 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.
Package details 


Author  Ze Vinicius [cre, aut], Daniel P. Palomar [aut] 
Maintainer  Ze Vinicius <jvmirca@gmail.com> 
License  GPL3 
Version  0.2.0 
URL  https://github.com/dppalomar/spectralGraphTopology https://mirca.github.io/spectralGraphTopology https://www.danielppalomar.com 
Package repository  View on CRAN 
Installation 
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