greedy_influential: Implements Greedy algorithm for Influence Maximization

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

Implements Greedy algorithm for Influence Maximization

Description

Implements Greedy algorithm for Influence Maximization

Usage

greedy_influential(graph, budget, prob = 0.5, test_method)

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"

Value

output containing summary

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.

Examples

{greedy_influential(erdos.renyi.game(500, 0.005), budget=5, prob=0.5, "RESILIENCE")}

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