Dynamic selection of activation targets to boost the influence spread in social networks

Cheng-Te Li, Man-Kwan Shan, Shou-De Lin

Abstract: This paper aims to combine the viral marketing with the idea of direct selling to for influence maximization in a social network. In direct selling, producers can sell the products directly to the consumers without having to go through a cascade of wholesalers. Through direct selling, it is possible to sell the products in a more efficient and economic manner. Motivated by this idea, we propose a target-selecting independent cascade (TIC) model, in which during influence propagation each active node can give up to attempt to influence some neighboring nodes, named victims, who could be hard to affect, and try to activate some of its friends of friends, termed destinations, who could have higher potential to increase the influence spread. Thus, the next question to ask is that given a social network and a set of seeds for influence propagation under TIC model, how to select targets (i.e., victims and destinations) for the attempts of activation during the propagation to boost of influence spread. We propose and evaluate three heuristics for the target selection. Experiments show that selecting targets based on influence probability between nodes have the highest boost of influence spread.