# 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 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.

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 = .5, verbose = FALSE, abstol = 1e-3)

# plot
# build network
# 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] == V(net)\$cluster[x],
colors[V(net)\$cluster[x]], '#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

`cluster_k_component_graph` N., Feiping, W., Xiaoqian, J., Michael I., and H., Heng. (2016). The Constrained Laplacian Rank Algorithm for Graph-based Clustering, AAAI’16.
`learn_laplacian_gle_mm` 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
`learn_laplacian_gle_admm` 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