Finding Influential Individuals in Social Networks Using Deep Reinforcement Learning to Tune the Neighborhood Selection Operator in the Variable Neighborhood Search Algorithm

Through social networks, which are groups of individuals and their relationships, people are often influenced by one another. Each individual in the network may propagate their behavior or ideas to those they are connected with. Thus, influence propagation occurs when a group of individuals exhibits a particular behavior or idea, and it spreads through the network due to interpersonal connections. Advertising, marketing, and public health can benefit from studying this phenomenon. The aim of this study is to pinpoint the most influential individuals in a social network so they can maximize their impact. As a result of the proposed method (DQVNS), the variable neighborhood search algorithm is improved by combining deep reinforcement learning (RL) and variable neighborhood search algorithms. Extensive evaluations on real social networks of diverse sizes confirm this algorithm's significant advantage over traditional heuristic approaches.