Efficient Methods to Select Top-K propagators based on Distance and Radius Neighbor.

The problem of influence maximization aims to select Top-K propagators according to the available budget in which when these propagators are selected the influence coverage is maximized. While most existing approaches aim to increase the influence, spread based on centrality measures only, without considering the distance in which each selected seed should be far away from another seed and hence can reduce significantly the influence spread performance. This observation motivated us to design two new algorithms that are based on a new proposed metrics namely Radius-Neighborhood Degree and Radius-Weighted Edges. The proposed two new algorithms are for selecting Top-K propagators named Radius-neighborhood degree over RND d-hops algorithm and combined radius-propagation probability threshold over and RND d-hops named CPRND d-hops algorithm. The RND d-hops algorithm selects Top-K propagators over d-hops, based on Radius-Neighborhood Degree metric that counts the degree of nodes till graph radius. The CPRND d-hops algorithm selects Top-K propagators to improve the selection of RND d-hops algorithm of Top-K propagators. The CPRND d-hops algorithm is based on Radius-Weighted Edges metric, that counts only the nodes edges which satisfy a certain propagation threshold considering RND d-hops algorithm selection. We conducted various experiments on large-scale graphs and compared them to the existing state of art approaches. The experimental results demonstrated that the proposed algorithms outperform the existing algorithms in term of influence achieved.