Identify Hub Nodes in a Network — find

This function identifies hub nodes in an igraph network based on degree and betweenness centrality using either z-score or quantile thresholds. Optionally, it visualizes the classification using a scatter plot with marginal histograms.

Usage

find_hubs(
  graph,
  method = c("zscore", "quantile"),
  degree_threshold = 3,
  betweenness_threshold = 1,
  degree_quantile = 0.95,
  betweenness_quantile = 0.95,
  log_transform = TRUE,
  plot = TRUE,
  focus_color = "darkgreen",
  label.size = 12,
  hub_names = TRUE,
  hub_cex = 3,
  gg_extra = list(),
  weights = NULL,
  weight_type = c("strength", "distance")
)

Arguments

graph: An igraph object or a data frame containing a symbolic edge list in the first two columns. Additional columns are considered as edge attributes.
method: Character. Method to identify hubs: "zscore" (standardized metrics) or "quantile" (empirical percentiles).
degree_threshold: Numeric. Threshold for standardized degree (only used if method = "zscore").
betweenness_threshold: Numeric. Threshold for standardized betweenness (only used if method = "zscore").
degree_quantile: Numeric between 0 and 1. Quantile threshold for degree (used if method = "quantile").
betweenness_quantile: Numeric between 0 and 1. Quantile threshold for betweenness (used if method = "quantile").
log_transform: Logical. If TRUE, applies log-transformation to degree and betweenness metrics.
plot: Logical. If TRUE, generates a plot of the degree vs. betweenness classification.
focus_color: Character. Color to display in the focus area of the plot (hubs region).
label.size: Numeric. Base font size for plot elements. Passed to theme_classic.
hub_names: Logical. If TRUE, adds node labels to identified hubs on the plot.
hub_cex: Numeric. Font size scaling factor for hub labels on the plot.
gg_extra: List. Additional user-defined layers for the returned ggplot. eg. list(ylim(-2,2), theme_bw(), theme(legend.position = "none"))
weights: Optional edge weights: NULL (default) to ignore them, the name of an edge attribute, or a numeric vector of length igraph::ecount(graph). When supplied, degree becomes vertex strength and betweenness is computed on edge costs. See netkit-weights.
weight_type: Either "strength" (default) or "distance". See netkit-weights.

Value

A list with the following components:

plot: A scatter plot of degree vs. betweenness with hub nodes highlighted, or NULL when plot = FALSE. The element is always present, so the return shape does not depend on the arguments. Note this is a ggExtraPlot (a grid gtable), not a ggplot: the scatterplot is passed through ggMarginal() to add the marginal histograms, which assembles it, so it can be printed but not extended with +. To customize it, pass layers through gg_extra; those are added before the assembly step.
method: Description of the method and thresholds used.
result: A tibble with node name, degree, betweenness, transformed metrics, and hub status.
graph: The original graph with a new vertex attribute is_hub.

Edge weights

Functions that can use edge weights take two arguments:

weights: NULL (the default) to ignore edge weights; the name of an edge attribute, such as "weight"; or a numeric vector with one value per edge, in igraph::E(graph) order.
weight_type: "strength" (the default) if a larger value means a more tightly connected pair – confidence scores, correlations, co-expression, read counts, interaction scores. "distance" if a larger value means further apart – costs, dissimilarities, reaction times.

Declaring which you have is not bookkeeping. igraph reads the weight attribute implicitly and gives it opposite meanings in different functions: a cost in igraph::betweenness(), igraph::distances(), igraph::diameter() and igraph::mean_distance(), but a strength in igraph::cluster_louvain() and the other community detection algorithms. Attaching a confidence score and letting that happen implicitly therefore inverts every path-based metric – a high-confidence interaction is treated as a long distance – while community detection reads the same numbers the way you intended.

netkit resolves weights and weight_type once per call and derives both a strength and a distance vector from them, so each metric receives the one it needs. A "strength" is converted to a distance by reciprocal (\(1/w\)); a "distance" is converted to a strength by reflection (\(\max(w) - w + \min(w)\)), which keeps a zero distance finite.

Examples

g <- igraph::sample_pa(80, power = 1.5, directed = FALSE)
res <- find_hubs(g, method = "quantile", plot = FALSE)
res$method
#> [1] "Hub nodes identified by method: quantile with Degree metric threshold = 1.63293809389639 and Betweenness metric threshold = 0.213719350028092 (unweighted)"
head(res$result)
#> # A tibble: 6 × 7
#>   node  degree strength betweenness degree_metric betweenness_metric is_hub
#>   <chr>  <dbl>    <dbl>       <dbl>         <dbl>              <dbl> <lgl> 
#> 1 1         33       33      0.887           3.53             0.635  TRUE  
#> 2 2          8        8      0.237           2.20             0.213  FALSE 
#> 3 3          4        4      0.418           1.61             0.349  FALSE 
#> 4 4          7        7      0.258           2.08             0.229  TRUE  
#> 5 5          2        2      0.0253          1.10             0.0250 FALSE 
#> 6 6          2        2      0.0500          1.10             0.0488 FALSE 

# The returned graph carries an `is_hub` vertex attribute, so results chain.
table(igraph::V(res$graph)$is_hub)
#> 
#> FALSE  TRUE 
#>    77     3