spectralGraphTopology-package: Package spectralGraphTopology

In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

Package spectralGraphTopology

Description

This package provides 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 community detection.

Functions

learn_k_component_graph learn_bipartite_graph learn_bipartite_k_component_graph cluster_k_component_graph learn_laplacian_gle_mm learn_laplacian_gle_admm L A

Help

For a quick help see the README file: GitHub-README.

Author(s)

Ze Vinicius and Daniel P. Palomar

References

S. Kumar, J. Ying, J. V. de Miranda Cardoso, and D. P. Palomar (2019). <https://arxiv.org/abs/1904.09792>

N., Feiping, W., Xiaoqian, J., Michael I., and H., Heng. (2016). The Constrained Laplacian Rank Algorithm for Graph-based Clustering, AAAI'16. <http://dl.acm.org/citation.cfm?id=3016100.3016174>

Licheng Zhao, Yiwei Wang, Sandeep Kumar, and Daniel P. Palomar. Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM IEEE Trans. on Signal Processing, vol. 67, no. 16, pp. 4231-4244, Aug. 2019

spectralGraphTopology documentation built on March 18, 2022, 7:35 p.m.