clusterGraph: Topological graph clustering

In SEMgraph: Network Analysis and Causal Inference Through Structural Equation Modeling

Topological graph clustering

Description

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:

1. "gHM", subgraph containing hidden modules as an igraph object;

2. "membership", cluster membership vector for each node.

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

Author(s)

Mario Grassi [e-mail]

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>

See Also

clusterScore, cplot

Examples

# Clustering ALS graph with WTC method and LV model
G <- properties(alsData$graph)[[1]]
clv <- clusterGraph(graph = G, type = "wtc", HM = "LV")
gplot(clv$gHM, l = "fdp")
table(clv$membership)

SEMgraph documentation built on Sept. 11, 2024, 8:36 p.m.