Speaker
Description
In the influence maximization problem we are interested in finding a given set of k vertices to activate in order to maximize the expected number of nodes that will eventually get activated in the network through a diffusion process. For this diffusion we assume the linear threshold model. We propose a new Clustering Greedy Algorithm (C-Greedy). The C-Greedy algorithm applies the Monte Carlo Greedy algorithm to partition the given network into clusters, and obtains the seed set from a combination of the seed set of each cluster by solving an integer linear program. Experimental results show that the C-Greedy algorithm has, in average, provided higher influence spread and lower running times than the classical Greedy algorithm on Watts-Strogatz random graphs, especially when the number of nodes is higher than 5000. C-Greedy was implemented in Julia language and used many Julia packages