Calculate node and edge centrality
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. Further tidygraph
provides access to the netrankr engine for centrality calculations and
define a number of centrality measures based on that, as well as provide a
manual mode for specifying more-or-less any centrality score.
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) centrality_manual(relation = "dist_sp", aggregation = "sum", ...) centrality_closeness_harmonic() centrality_closeness_residual() centrality_closeness_generalised(alpha) centrality_integration() centrality_communicability() centrality_communicability_odd() centrality_communicability_even() centrality_subgraph_odd() centrality_subgraph_even() centrality_katz(alpha = NULL) centrality_betweenness_network(netflowmode = "raw") centrality_betweenness_current() centrality_betweenness_communicability() centrality_betweenness_rsp_simple(rspxparam = 1) centrality_betweenness_rsp_net(rspxparam = 1) centrality_information() centrality_decay(alpha = 1) centrality_random_walk() centrality_expected()
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. |
relation |
The indirect relation measure type to be used in |
aggregation |
The aggregation type to use on the indirect relations to be used in |
... |
Arguments to pass on to |
netflowmode |
The return type of the network flow distance, either |
rspxparam |
inverse temperature parameter |
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()
centrality_manual: Manually specify your centrality score using the netrankr framework (netrankr)
centrality_closeness_harmonic: centrality based on inverse shortest path (netrankr)
centrality_closeness_residual: centrality based on 2-to-the-power-of negative shortest path (netrankr)
centrality_closeness_generalised: centrality based on alpha-to-the-power-of negative shortest path (netrankr)
centrality_integration: centrality based on 1 - (x - 1)/max(x) transformation of shortest path (netrankr)
centrality_communicability: centrality an exponential tranformation of walk counts (netrankr)
centrality_communicability_odd: centrality an exponential tranformation of odd walk counts (netrankr)
centrality_communicability_even: centrality an exponential tranformation of even walk counts (netrankr)
centrality_subgraph_odd: subgraph centrality based on odd walk counts (netrankr)
centrality_subgraph_even: subgraph centrality based on even walk counts (netrankr)
centrality_katz: centrality based on walks penalizing distant nodes (netrankr)
centrality_betweenness_network: Betweenness centrality based on network flow (netrankr)
centrality_betweenness_current: Betweenness centrality based on current flow (netrankr)
centrality_betweenness_communicability: Betweenness centrality based on communicability (netrankr)
centrality_betweenness_rsp_simple: Betweenness centrality based on simple randomised shortest path dependencies (netrankr)
centrality_betweenness_rsp_net: Betweenness centrality based on net randomised shortest path dependencies (netrankr)
centrality_information: centrality based on inverse sum of resistance distance between nodes (netrankr)
centrality_decay: based on a power transformation of the shortest path (netrankr)
centrality_random_walk: centrality based on the inverse sum of expected random walk length between nodes (netrankr)
centrality_expected: Expected centrality ranking based on exact rank probability (netrankr)
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())Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.