optimal_influential: Implements optimal algorithm for Influence Maximization

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

Implements optimal algorithm for Influence Maximization

Description

Implements optimal algorithm for Influence Maximization

Usage

optimal_influential(
  graph,
  budget,
  prob = 0.5,
  test_method = c("RESILIENCE", "INFLUENCE_LT", "INFLUENCE_IC"),
  parallel = TRUE
)

Arguments

graph	is the igraph object
budget	number of influential nodes to be fetched. Default value is 1
prob	probability at which a node influences its neighbours. In case of INFLUENCE_LT, this is becomes the threshold value. Default is 0.5
test_method	specifies the method to measure influence. Value MUST be "RESILIENCE", "INFLUENCE_IC" or "INFLUENCE_LT"
parallel	when true, executes the funtion using multiple CPU cores. Default value is TRUE

Value

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

References

Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the Spread of Influence through a Social Network. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’03, 137.

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