Using swarm intelligence algorithms to detect influential individuals for influence maximization in social networks

In this study, a change in the structure of the IM problem is suggested in order to tailor it to swarm intelligence algorithms and to achieve a general slope on the state-space surface of its objective function. We named this process as “reshaping”. More precisely, if a social network is envisioned as a graph and individuals as nodes, reshaping means sorting the nodes in descending order (from largest to smallest) according to the metrics under consideration (i.e., metrics that give an idea about the level of influence of an individual) and renumbering the nodes according to this order. Thus, the nodes those are close to each other in terms of level of influence become closer to each other in the state-space. This creates a general slope on the state-space surface of the objective function. This simple idea paves the way for applying all swarm intelligence algorithms to this kind of problem. The proposed approach was tested with real and synthetic graphs. The experiments employed the Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) as the swarm intelligence algorithms and PageRank and Kempe et al.'s Greedy Algorithm as benchmark methods. Experimental results showed that this approach worked well.