Hypergraph negative influence blocking maximization via influence estimation

Abstract

The rapid advancement of internet technology has accelerated the spread of negative information. To block the impact of such diffusion, selecting a set of positive seed nodes for competitive propagation is a viable strategy. However, existing studies have predominantly focused on pairwise user interactions, neglecting group relationships. To address this gap, we employ a hypergraph to model higher-order group interactions, defining the hypergraph influence blocking maximization (HIBM) problem and proposing the hypergraph competitive susceptible–infected (HCSI) diffusion model. We then prove the monotonicity and submodularity of the objective function, enabling the development of a greedy algorithm. To address the high computational complexity of the greedy approach, we further propose an efficient heuristic method, hypergraph competitive influence estimation (HCIE), which leverages a probabilistic formula to estimate the influence of negative seed sets under the blocking effect of positive nodes, thereby selecting positive nodes to minimize the propagation of negative information. Experimental results demonstrate that the HCIE method reduces the diffusion of negative information by over 10% on certain datasets, showcasing its effectiveness and robustness. Additionally, the HCIE method achieves performance close to that of the greedy algorithm while significantly reducing computational time.