spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

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.

Package details

AuthorZe Vinicius [cre, aut], Daniel P. Palomar [aut]
MaintainerZe Vinicius <jvmirca@gmail.com>
URL https://github.com/dppalomar/spectralGraphTopology https://mirca.github.io/spectralGraphTopology https://www.danielppalomar.com
Package repositoryView on CRAN
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spectralGraphTopology documentation built on Oct. 12, 2019, 9:05 a.m.