The Influence Maximization (IM) problem is a well-known NP-hard combinatorial problem over graphs whose goal is to find the set of nodes in a network that spreads influence at most. Among the various methods for solving the IM problem, evolutionary algorithms (EAs) have been shown to be particularly effective. While the literature on the topic is particularly ample, only a few attempts have been made at solving the IM problem over higher-order networks, namely extensions of standard graphs that can capture interactions that involve more than two nodes. Hypergraphs are a valuable tool for modeling complex interaction networks in various domains; however, they require rethinking of several graph-based problems, including IM. In this work, we propose a multi-objective EA for the IM problem over hypergraphs that leverages smart initialization and hypergraph-aware mutation. While the existing methods rely on greedy or heuristic methods, to our best knowledge this is the first attempt at applying EAs to this problem. Our results over nine real-world datasets and three propagation models, compared with five baseline algorithms, reveal that our method achieves in most cases state-of-the-art results in terms of hypervolume and solution diversity.

翻译：影响力最大化（IM）问题是图论中一类著名的NP困难组合优化问题，其目标是寻找网络中最能传播影响力的节点集合。在求解IM问题的众多方法中，进化算法（EAs）已被证明尤为有效。尽管相关文献十分丰富，但针对高阶网络（即能捕获涉及两个以上节点交互关系的标准图扩展结构）求解IM问题的尝试仍然较少。超图作为建模各领域复杂交互网络的重要工具，需要对包括IM在内的诸多图论问题进行重新审视。本文提出了一种面向超图IM问题的多目标进化算法，该算法采用智能初始化策略与超图感知变异算子。现有方法多依赖贪婪或启发式算法，据我们所知，这是首次将进化算法应用于该问题的尝试。我们在九个真实数据集和三种传播模型上进行了实验，与五种基线算法对比发现：本文方法在多数情况下能在超体积与解多样性指标上取得最先进的结果。