Compute per-node centralities and attach them to a graph object

Computes a panel of standard per-node centrality measures and adds them as new vertex columns on the input `tbl_graph`. Use this for node-importance analysis when you want to rank nodes (or map a centrality to a visual aesthetic in [ggNetView()]) by something more informative than degree alone.

Usage

get_node_centrality(
  graph_obj,
  measures = c("Betweenness", "Closeness", "Eigenvector", "PageRank", "Hub_score",
    "Authority_score", "Coreness", "Harmonic"),
  weighted = FALSE,
  overwrite = TRUE
)

Arguments

graph_obj: A `tbl_graph` produced by any `build_graph_from_*()` constructor.
measures: Character vector. Which centralities to compute. Pass `"all"` to compute every supported measure. Defaults to all eight measures listed in Available measures.
weighted: Logical (default `FALSE`). If `TRUE`, the edge `weight` attribute is used as a distance (`igraph` convention: higher weight = farther). Because correlation networks have `weight = |correlation|` – where higher means *closer* – the distance used internally is `1 / weight` so that strongly correlated pairs count as short paths. Set `FALSE` (default) for the textbook unweighted versions.
overwrite: Logical (default `TRUE`). If a measure column already exists on the input graph, controls whether to overwrite it (silent overwrite when `TRUE`; warning + skip when `FALSE`).

Value

A `tbl_graph` whose node table is augmented with one column per requested measure (using the column names listed in Available measures). Other vertex / edge columns are preserved verbatim.

Details

Note that [get_network_topology()] reports the same families of metrics but as **network-level summaries** (a single mean / sum per network). `get_node_centrality()` is the per-node counterpart – it keeps every individual value so you can sort, rank, threshold, or colour by it.

Available measures

All measures wrap the corresponding `igraph` function:

`"Betweenness"`: Number of shortest paths through each node (`igraph::betweenness`).
`"Closeness"`: Inverse mean shortest-path distance from a node to every other node (`igraph::closeness`, `mode = "all"`). Returns `NaN` for nodes in their own connected component when the component is a singleton.
`"Eigenvector"`: Eigenvector centrality (`igraph::eigen_centrality`).
`"PageRank"`: Google PageRank score (`igraph::page_rank`).
`"Hub_score"`: HITS hub score (`igraph::hub_score`).
`"Authority_score"`: HITS authority score (`igraph::authority_score`).
`"Coreness"`: k-core membership of each vertex (`igraph::coreness`).
`"Harmonic"`: Harmonic centrality (`igraph::harmonic_centrality`); robust to disconnected graphs where Closeness becomes ill-defined.

Examples

# \donttest{
set.seed(1)
mat <- matrix(stats::rnorm(40 * 20), nrow = 40, ncol = 20)
rownames(mat) <- paste0("feature", seq_len(40))
colnames(mat) <- paste0("sample",  seq_len(20))
obj <- build_graph_from_mat(
  mat = mat, method = "cor", cor.method = "pearson",
  proc = "none", r.threshold = 0.3, p.threshold = 0.05
)
#> The max module in network is 6 we use the 6  modules for next analysis

obj_aug <- get_node_centrality(obj)
obj_aug %>%
  tidygraph::activate(nodes) %>%
  tidygraph::as_tibble() %>%
  dplyr::arrange(dplyr::desc(Betweenness)) %>%
  utils::head(5)
#> # A tibble: 5 × 15
#>   name      modularity modularity2 modularity3 Modularity Degree Strength
#>   <chr>     <fct>      <ord>       <chr>       <ord>       <dbl>    <dbl>
#> 1 feature40 5          5           5           5               4     2.06
#> 2 feature7  3          3           3           3               4     2.20
#> 3 feature36 2          2           2           2               6     3.23
#> 4 feature23 3          3           3           3               2     1.12
#> 5 feature39 4          4           4           4               3     1.56
#> # ℹ 8 more variables: Betweenness <dbl>, Closeness <dbl>, Eigenvector <dbl>,
#> #   PageRank <dbl>, Hub_score <dbl>, Authority_score <dbl>, Coreness <dbl>,
#> #   Harmonic <dbl>
# }