adaptive_centrality_influential: Returns the most influential nodes in a graph using adaptive...

In seekme94/influence.mining: Provides various methods for influence mining on Graphs

Returns the most influential nodes in a graph using adaptive centrality-based heuristics

Description

Returns the most influential nodes in a graph using adaptive centrality-based heuristics

Usage

adaptive_centrality_influential(
  graph,
  budget = 1,
  test_method = c("RESILIENCE", "INFLUENCE_LT", "INFLUENCE_IC"),
  centrality_method = c("DEGREE", "ECCENTRICITY", "AVERAGE_DISTANCE", "BARYCENTER",
    "BETWEENNESS", "BOTTLENECK", "CENTROID", "CLOSENESS", "CLUSTERRANK",
    "COMMUNITY_BETWEENNESS", "COMMUNITY_CENTRALITY", "CROSS_CLIQUE",
    "CURRENTFLOW_CLOSENESS", "DECAY", "EDGE_PERCOLATION", "EIGENVECTOR", "ENTROPY",
    "FREEMAN_CLOSENESS", "GEODESIC_K_PATH", "HUBBELL", "KATZ", "LAPLACIAN",
    "LATORA_CLOSENESS", "LEADERRANK", "LEVERAGE", "LINCENT", "LOBBY", "MARKOV",
    "MAX_NEIGHBORHOOD_COMPONENT", "MAX_NEIGHBORHOOD_DENSITY", "PAIRWISE_DISCONNECTIVITY",
         "RADIALITY", "RESIDUAL_CLOSENESS", "SALSA", "SEMILOCAL",
    "TOPOLOGICAL_COEFFICIENT", "VITALITY_CLOSENESS")
)

Arguments

graph	is the igraph object
budget	number of influential nodes to be fetched. Default value is 1
test_method	specifies the method to measure influence. Value MUST be "RESILIENCE", "INFLUENCE_IC" or "INFLUENCE_LT"
centrality_method	defines the centrality method to be used. Value must be:

Value

object containing: 1. Vector of influential nodes. 2. Measure of influence. 3. Elapsed time in seconds.

References

Lipton, R. J., & Naughton, J. F. (1989). Estimating the size of generalized transitive closures. In Proceedings of the 15th Int. Conf. on Very Large Data Bases.

seekme94/influence.mining documentation built on Aug. 2, 2022, 10:19 p.m.