| centrality {tidygraph} | R Documentation |
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.
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)
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 |
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. |
A numeric vector giving the centrality measure of each node.
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()
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())