R: Calculate node and edge centrality

centrality {tidygraph} | R Documentation

## Calculate node and edge centrality

### Description

The centrality of a node measures the importance of node in the network. As the concept of importance is ill-defined and dependent on the network and the questions under consideration, many centrality measures exist. `tidygraph` provides a consistent set of wrappers for all the centrality measures implemented in `igraph` for use inside `dplyr::mutate()` and other relevant verbs. All functions provided by `tidygraph` have a consistent naming scheme and automatically calls the function on the graph, returning a vector with measures ready to be added to the node data.

### Usage

    centrality_alpha(weights = NULL, alpha = 1, exo = 1, tol = 1e-07,
      loops = FALSE)

    centrality_authority(weights = NULL, scale = TRUE,
      options = igraph::arpack_defaults)

    centrality_betweenness(weights = NULL, directed = TRUE, cutoff = NULL,
      nobigint = TRUE, normalized = FALSE)

    centrality_power(exponent = 1, rescale = FALSE, tol = 1e-07,
      loops = FALSE)

    centrality_closeness(weights = NULL, mode = "out", normalized = FALSE,
      cutoff = NULL)

    centrality_eigen(weights = NULL, directed = FALSE, scale = TRUE,
      options = igraph::arpack_defaults)

    centrality_hub(weights = NULL, scale = TRUE,
      options = igraph::arpack_defaults)

    centrality_pagerank(weights = NULL, directed = TRUE, damping = 0.85,
      personalized = NULL)

    centrality_subgraph(loops = FALSE)

    centrality_degree(weights = NULL, mode = "out", loops = TRUE,
      normalized = FALSE)

    centrality_edge_betweenness(weights = NULL, directed = TRUE,
      cutoff = NULL)

### Arguments

`weights` | The weight of the edges to use for the calculation. Will be evaluated in the context of the edge data.
`alpha` | Relative importance of endogenous vs exogenous factors
`exo` | The exogenous factors of the nodes. Either a scalar or a number number for each node. Evaluated in the context of the node data.
`tol` | Tolerance for near-singularities during matrix inversion
`loops` | Should loops be included in the calculation
`scale` | Should the output be scaled between 0 and 1
`options` | Settings passed on to `igraph::arpack()`
`directed` | Should direction of edges be used for the calculations
`cutoff` | maximum path length to use during calculations
`nobigint` | Should big integers be avoided during calculations
`normalized` | Should the output be normalized
`exponent` | The decay rate for the Bonacich power centrality
`rescale` | Should the output be scaled to sum up to 1
`mode` | How should edges be followed. Ignored for undirected graphs
`damping` | The damping factor of the page rank algorithm
`personalized` | The probability of jumping to a node when abandoning a random walk. Evaluated in the context of the node data.

### Value

A numeric vector giving the centrality measure of each node.

### Functions

- `centrality_alpha`: Wrapper for `igraph::alpha_centrality()`

- `centrality_authority`: Wrapper for `igraph::authority_score()`

- `centrality_betweenness`: Wrapper for `igraph::betweenness()` and `igraph::estimate_betweenness()`

- `centrality_power`: Wrapper for `igraph::power_centrality()`

- `centrality_closeness`: Wrapper for `igraph::closeness()` and `igraph::estimate_closeness()`

- `centrality_eigen`: Wrapper for `igraph::eigen_centrality()`

- `centrality_hub`: Wrapper for `igraph::hub_score()`

- `centrality_pagerank`: Wrapper for `igraph::page_rank()`

- `centrality_subgraph`: Wrapper for `igraph::subgraph_centrality()`

- `centrality_degree`: Wrapper for `igraph::degree()` and `igraph::strength()`

- `centrality_edge_betweenness`: Wrapper for `igraph::edge_betweenness()`

### Examples

    create_notable('bull') %>%
      activate(nodes) %>%
      mutate(importance = centrality_alpha())

    # Most centrality measures are for nodes but not all
    create_notable('bull') %>%
      activate(edges) %>%
      mutate(importance = centrality_edge_betweenness())

[Package tidygraph version 1.0.0 Index]