Learning Graphs from Data via Spectral Constraints.

spectralGraphTopology

spectralGraphTopology 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 leverage 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.

Documentation: https://mirca.github.io/spectralGraphTopology.

Installation

From inside an R session, type:

> install.packages("spectralGraphTopology")

Alternatively, you can install the development version from GitHub:

> devtools::install_github("dppalomar/spectralGraphTopology")

Microsoft Windows

On MS Windows environments, make sure to install the most recent version of Rtools.

macOS

spectralGraphTopology depends on RcppArmadillo which requires gfortran.

Usage: clustering

We illustrate the usage of the package with simulated data, as follows:

library(spectralGraphTopology)
library(clusterSim)
library(igraph)
set.seed(42)

# generate graph and data
n <- 50  # number of nodes per cluster
twomoon <- clusterSim::shapes.two.moon(n)  # generate data points
k <- 2  # number of components

# estimate underlying graph
S <- crossprod(t(twomoon$data))
graph <- learn_k_component_graph(S, k = k, beta = .25, verbose = FALSE, abstol = 1e-3)

# plot
# build network
net <- igraph::graph_from_adjacency_matrix(graph$adjacency, mode = "undirected", weighted = TRUE)
# colorify nodes and edges
colors <- c("#706FD3", "#FF5252")
V(net)$cluster <- twomoon$clusters
E(net)$color <- apply(as.data.frame(get.edgelist(net)), 1,
                      function(x) ifelse(V(net)$cluster[x[1]] == V(net)$cluster[x[2]],
                                        colors[V(net)$cluster[x[1]]], '#000000'))
V(net)$color <- colors[twomoon$clusters]
# plot nodes
plot(net, layout = twomoon$data, vertex.label = NA, vertex.size = 3)

Contributing

We welcome all sorts of contributions. Please feel free to open an issue to report a bug or discuss a feature request.

Citation

If you made use of this software please consider citing:

• J. V. de Miranda Cardoso, D. P. Palomar (2019). spectralGraphTopology: Learning Graphs from Data via Spectral Constraints. https://CRAN.R-project.org/package=spectralGraphTopology

• S. Kumar, J. Ying, J. V. de Miranda Cardoso, and D. P. Palomar (2020). A unified framework for structured graph learning via spectral constraints. Journal of Machine Learning Research (21), pages 1-60.

• S. Kumar, J. Ying, J. V. de Miranda Cardoso, D. P. Palomar (2019). Structured graph learning via Laplacian spectral constraints. Advances in Neural Information Processing Systems.

In addition, consider citing the following bibliography according to their implementation:

Links

Package: CRAN and GitHub

README file: GitHub-readme

Vignette: GitHub-html-vignette, CRAN-html-vignette, NeurIPS’19 Promotional slides, NeurIPS’19 Promotional video.