超图中影响力最大化的通用元启发式框架
A universal meta-heuristic framework for influence maximization in hypergraphs
AI总结：
针对超图中的影响力最大化问题，提出基于遗传进化的元启发式框架HGA，实验证明其性能优于基线方法，并揭示种子节点选择需结合多种拓扑特征。
摘要：
影响力最大化（IM）旨在选择少量节点，使其能够在网络中最大化自身影响力，并涵盖广泛的应用场景。尽管已有大量尝试在普通网络中提供有效解决方案，但现实世界各类系统中实体间的高阶交互通常未被纳入考量。本文提出一种通用的元启发式方法——超图遗传算法（HGA），以解决超图中的IM问题，该方法基于遗传进化概念。在简单和复杂传染模型下的合成与经验超图中进行的系统验证表明，与基线方法相比，HGA取得了通用且合理的性能表现。我们通过消融研究和相关性分析探究了HGA优异性能的成因。研究结果表明，HGA的解与其他既有方法的解存在显著差异。此外，通过深入观察不同算法所获种子节点的局部拓扑特征发现，种子节点的选择不能仅依赖单一拓扑特征，而应结合多种拓扑特征来解决IM问题。
英文摘要：
Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this paper, we propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to tackle the IM problem in hypergraphs, which is based on the concept of genetic evolution. Systematic validations in synthetic and empirical hypergraphs under both simple and complex contagion models indicate that HGA achieves universal and plausible performance compared to baseline methods. We explore the cause of the excellent performance of HGA through ablation studies and correlation analysis. The findings show that the solution of HGA is distinct from that of other prior methods. Moreover, a closer look at the local topological features of the seed nodes acquired by different algorithms reveals that the selection of seed nodes cannot be based on a single topological characteristic, but should involve a combination of multiple topological features to address the IM problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
