HIDE-EA: an Influence Decaying Estimation-based Evolutionary Algorithm for Hypergraph Dynamic Influence Maximization

With the rapid growth of social networks, user interactions have become increasingly complex, making traditional binary relationship-based methods inadequate for studying group social relationships. Moreover, existing research has overlooked the dynamic decay of information propagation among users. To address this, we define the Hypergraph Dynamic Influence Maximization (HDIM) problem and develop a Hypergraph Dynamic Susceptible-Infected (HDSI) model to capture the dynamic decay of information spread. For solving HDIM problem, we propose a Hypergraph Influence Decaying Estimation-based Evolutionary Algorithm (HIDE-EA). This approach formulates an objective function using probabilistic influence decaying estimation and iteratively optimizes the node set as the decision variable, effectively expanding influence propagation in hypergraphs while greatly reducing the computational cost of Monte Carlo simulations. We also design a Node Influence Decaying Estimation (NIDE)-based initialization method to accelerate convergence. Experiments on hypergraph datasets show that HIDE-EA significantly shortens computation time while achieving results comparable to the greedy algorithm, and outperforms other baseline methods.