Influence maximization in social networks

People use online social networks to spread ideas, learn about innovations, etc. In this context, it is important to know how information spreads through social networks. It is possible to spread information (e.g., product advertisement) to a larger number of individuals via a social network. The key point is to identify the most influential individuals on the social network. This problem is named as Influence Maximization (IM) problem. The IM problem focuses on finding the small subset of individuals in a social environment who influence a certain group of individuals. In the literature, greedy, stochastic, and evolutionary optimization algorithms have been proposed to solve this problem. However, these methods are not at the desired level in terms of speed or solution quality. On the other hand, although many Swarm Intelligence (SI) algorithms can be found in the literature, these algorithms cannot be directly applied to the IM problem. In this thesis, a change in the structure of the IM problem is suggested in order to tailor it to SI algorithms. If a social network is envisioned as a graph and individuals as nodes, the proposed method means sorting the nodes in descending order according to some graph metrics and renumbering the nodes according to this order. The proposed approach was tested with signed and unsigned real and synthetic graphs. The experiments employed Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) SI algorithms and PageRank and Kempe et. al.'s Greedy Algorithm as benchmark methods. Experimental results showed that this approach worked well.