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.
From inside an R session, type:
Alternatively, you can install the development version from GitHub:
On MS Windows environments, make sure to install the most recent version of
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 datapoints k <- 2 # number of components # estimate underlying graph S <- crossprod(t(twomoon$data)) graph <- learn_k_component_graph(S, k = k, beta = .5, verbose = FALSE, abstol = 1e-3) # plot # build network net <- igraph::graph_from_adjacency_matrix(graph$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] == 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)
We welcome all sorts of contributions. Please feel free to open an issue to report a bug or discuss a feature request.
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. R package version 0.1.0. https://CRAN.R-project.org/package=spectralGraphTopology
S. Kumar, J. Ying, J. V. de Miranda Cardoso, and D. P. Palomar (2019). A unified framework for structured graph learning via spectral constraints. https://arxiv.org/abs/1904.09792
In case you made use of the function
cluster_k_component_graph, consider citing:
- N., Feiping, W., Xiaoqian, J., Michael I., and H., Heng. (2016). The Constrained Laplacian Rank Algorithm for Graph-based Clustering, AAAI'16. https://dl.acm.org/citation.cfm?id=3016100.3016174