Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization

2026-04-17

AI Summary

This work addresses the limitations of traditional influence maximization approaches in capturing high-order interactions inherent in real-world systems. While hypergraphs offer a natural framework for modeling such complex relationships, they introduce significant challenges, including an exponentially large search space and intricate cascade dynamics. To tackle these issues, the paper proposes a novel hypergraph-based influence maximization method that integrates discrete particle swarm optimization with a threshold propagation model. Each particle encodes a candidate seed set, and its fitness is efficiently evaluated via a two-layer local influence approximation. The approach further incorporates a degree-centrality-guided initialization strategy and a velocity-position update mechanism enhanced with local search. Extensive experiments on both synthetic and real-world hypergraph datasets demonstrate the superiority of the proposed method over existing baselines, while ablation studies confirm the effectiveness of its key components.

Technology Category

• Search and Optimization: Heuristic Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Influence maximization (IM) is a fundamental problem in complex network analysis, with a wide range of real-world applications. To date, existing approaches to influential node identification in IM have predominantly relied on standard graphs, failing to capture higher-order intrinsic interactions embedded in many real-world systems. Hypergraphs can be employed to better capture higher-order interactions. However, using hypergraphs may lead to an excessively large search space and increased complexity in modeling cascading dynamics, making it challenging to accurately identify influential nodes. Therefore, in this study, we propose a new hypergraph-modeled IM method, based on the Discrete Particle Swarm Optimization algorithm and the threshold model. In the proposed method, a particle (i.e., a candidate solution) represents the selection information of seed nodes, and the fitness function is designed to accurately and efficiently evaluate the influence of seed nodes via a two-layer local influence approximation. We also propose a degree-based initialization strategy to improve the quality of initial solutions and develop rules for updating particles' velocity and position, incorporated with a local search to drive particles toward better solutions. Experimental results demonstrate that the proposed method outperforms baseline methods on both synthetic and real-world hypergraphs. In addition, ablation studies validate the effectiveness of both the local search and the initialization strategies.

Problem

• Influence Maximization
• Hypergraph
• Higher-order Interactions
• Seed Node Identification
• Complex Network Analysis

Innovation

• Hypergraph-modeled Influence Maximization
• Discrete Particle Swarm Optimization
• Two-layer Local Influence Approximation
• Degree-based Initialization
• Local Search