Guided Genetic Algorithm for the Influence Maximization Problem

摘要: Influence maximization is a hard combinatorial optimization problem. It requires the identification of an optimum set k network vertices that triggers activation maximum total number remaining nodes with respect to chosen propagation model. The problem appealing because it provably and has practical applications in domains such as data mining social analysis. Although there are many exact heuristic algorithms for influence maximization, been tackled by metaheuristic evolutionary methods well. This paper presents evaluates new method employs recent genetic algorithm fixed–length subset selection. extended concept guiding prevents selection infeasible vertices, reduces search space, effectively improves procedure.