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Native graph community detection

Source: R/graph.R
graph_cluster.Rd

graph_cluster() clusters a compact fastEmbedR_graph with a native multilevel Louvain implementation, a Leiden local-move/refine/aggregate implementation, or the Pons-Latapy Walktrap algorithm. Louvain and Leiden have CPU, CUDA, and Metal implementations. Walktrap is CPU-only. No Python, igraph, cuGraph, or external clustering function is called at run time.

Usage

graph_cluster(
  graph,
  method = c("leiden", "louvain", "walktrap"),
  backend = NULL,
  resolution = 1,
  n_iterations = 10L,
  n_runs = 1L,
  steps = 4L,
  seed = 1L
)

Arguments

graph

A fastEmbedR_graph, or an edge-list list containing from, to, weight, and n_vertices.

method

"leiden", "louvain", or "walktrap". Walktrap is the package's true random-walk community method.

backend

Execution backend. "cuda" and "metal" are available for Louvain and Leiden only and fail explicitly if the requested accelerator was not compiled or is unavailable. They never fall back to CPU.

resolution

Positive modularity resolution for Louvain and Leiden. Walktrap uses its reference resolution of 1.

n_iterations

Maximum local-moving passes for Louvain and Leiden.

n_runs

Independent seeded Louvain/Leiden runs; the highest-modularity result is returned.

steps

Random-walk length for Walktrap.

seed

Reproducible random seed for Louvain and Leiden.

Value

A fastEmbedR_graph_cluster containing one-based membership, modularity, method, implementation, and run metadata.

References

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;2008:P10008.

Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9:5233.

Pons P, Latapy M. Computing communities in large networks using random walks. J Graph Algorithms Appl. 2006;10:191-218.

Examples

x <- scale(as.matrix(iris[, 1:4]))
graph <- knn_graph(x, k = 15)
clusters <- graph_cluster(graph, method = "leiden", seed = 1)
table(clusters$membership)
#> 
#>  1  2  3  4  5  6 
#> 33 17 29 21 20 30