centrality | 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. 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. These measures
all only work on undirected graphs.
centrality_alpha(
weights = NULL,
alpha = 1,
exo = 1,
tol = 1e-07,
loops = FALSE
)
centrality_authority(weights = NULL, scale = TRUE, options = arpack_defaults())
centrality_betweenness(
weights = NULL,
directed = TRUE,
cutoff = -1,
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 = arpack_defaults()
)
centrality_hub(weights = NULL, scale = TRUE, options = 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_harmonic(
weights = NULL,
mode = "out",
normalized = FALSE,
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 |
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()
centrality_power()
: Wrapper for igraph::power_centrality()
centrality_closeness()
: Wrapper for igraph::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_harmonic()
: Wrapper for igraph::harmonic_centrality()
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())
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