centrality

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.

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)
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()

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()

• 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)

Aliases

• centrality
• centrality_alpha
• centrality_authority
• centrality_betweenness
• centrality_power
• centrality_closeness
• centrality_eigen
• centrality_hub
• centrality_pagerank
• centrality_subgraph
• centrality_degree
• centrality_edge_betweenness
• centrality_manual
• centrality_closeness_harmonic
• centrality_closeness_residual
• centrality_closeness_generalised
• centrality_integration
• centrality_communicability
• centrality_communicability_odd
• centrality_communicability_even
• centrality_subgraph_odd
• centrality_subgraph_even
• centrality_katz
• centrality_betweenness_network
• centrality_betweenness_current
• centrality_betweenness_communicability
• centrality_betweenness_rsp_simple
• centrality_betweenness_rsp_net
• centrality_information
• centrality_decay
• centrality_random_walk
• centrality_expected

Examples

# NOT RUN {
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())
# }