A Heuristic Mutation Particle Swarm Optimization Algorithm for Budgeted Influence Maximization with Emotional Competition

The budgeted influence maximization (BIM) problem aims to select an optimal subset of nodes, each with a unique selection cost, to maximize the influence in a network under a fixed budget. Most existing methods address static networks with constant node infection states, but usually dismiss the dynamic interaction of competing emotions within the network. This work extends the BIM problem to account for dynamic emotional competitions. Specifically, we first formally formulate the BIM problem in a competitive environment where emotions dynamically influence and transform each other over time. Then, we introduce a local structure-sensitive heuristic function designed to evaluate a node’s influence potential in a competitive environment. Furthermore, we propose a heuristic mutation particle swarm optimization (HMPSO) algorithm to identify a set of high-impact nodes, thereby maximizing desirable information spread. Experimental results conducted on two real-world networks demonstrate that the HMPSO algorithm outperforms existing advanced methods for the BIM problem.