Topological graph clustering

Topological graph clustering methods.

Usage

clusterGraph(graph, type = "wtc", HM = "none", size = 5, verbose = FALSE, ...)

Arguments

graph: An igraph object.
type: Topological clustering methods. If type = "tahc", network modules are generated using the tree agglomerative hierarchical clustering method (Yu et al., 2015). Other non-tree clustering methods from igraph package include: "wtc" (default value; walktrap community structure with short random walks), "ebc" (edge betweeness clustering), "fgc" (fast greedy method), "lbc" (label propagation method), "lec" (leading eigenvector method), "loc" (multi-level optimization), "opc" (optimal community structure), "sgc" (spinglass statistical mechanics).
HM: Hidden model type. Enables the visualization of the hidden model, gHM. If set to "none" (default), no gHM igraph object is saved. For each defined hidden module: (i) if HM = "LV", a latent variable (LV) will be defined as common unknown cause acting on cluster nodes; (ii) if HM = "CV", cluster nodes will be considered as regressors of a latent composite variable (CV); (iii) if HM = "UV", an unmeasured variable (UV) is defined, where source nodes of the module (i.e., in-degree = 0) act as common regressors influencing the other nodes via an unmeasured variable (see also clusterScore).
size: Minimum number of nodes per module. By default, a minimum number of 5 nodes is required.
verbose: A logical value. If FALSE (default), the gHM igraph will not be plotted to screen, saving execution time (they will be returned in output anyway).
...: Currently ignored.

Value

If HM is not "none" a list of 2 objects is returned:

"gHM", subgraph containing hidden modules as an igraph object;
"membership", cluster membership vector for each node.

If HM is "none", only the cluster membership vector is returned.

References

Fortunato S, Hric D. Community detection in networks: A user guide (2016). Phys Rep; 659: 1-44. <https://dx.doi.org/10.1016/j.physrep.2016.09.002>

Yu M, Hillebrand A, Tewarie P, Meier J, van Dijk B, Van Mieghem P, Stam CJ (2015). Hierarchical clustering in minimum spanning trees. Chaos 25(2): 023107. <https://doi.org/10.1063/1.4908014>

Author

Mario Grassi mario.grassi@unipv.it

Examples


# Clustering ALS graph with WTC method and LV model
G <- properties(alsData$graph)[[1]]
#> Frequency distribution of graph components
#> 
#>   n.nodes n.graphs
#> 1      32        1
#> 
#> Percent of vertices in the giant component: 100 %
#> 
#>   is.simple      is.dag is.directed is.weighted 
#>        TRUE        TRUE        TRUE        TRUE 
#> 
#> which.mutual.FALSE 
#>                 47 
clv <- clusterGraph(graph = G, type = "wtc", HM = "LV")
#> modularity = 0.5588502 
#> 
#> Community sizes
#>  3  1  4  2 
#>  4  8  9 11 
#> 
gplot(clv$gHM, l = "fdp")

table(clv$membership)
#> 
#>  1  2  4 
#>  8 11  9