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Influence maximization (IM) aims to select a small number of nodes that areable to maximize their influence in a network and covers a wide range ofapplications. Despite numerous attempts to provide effective solutions inordinary networks, higher-order interactions between entities in variousreal-world systems are not usually taken into account. In this paper, wepropose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), totackle the IM problem in hypergraphs, which is based on the concept of geneticevolution. Systematic validations in synthetic and empirical hypergraphs underboth simple and complex contagion models indicate that HGA achieves universaland plausible performance compared to baseline methods. We explore the cause ofthe excellent performance of HGA through ablation studies and correlationanalysis. The findings show that the solution of HGA is distinct from that ofother prior methods. Moreover, a closer look at the local topological featuresof the seed nodes acquired by different algorithms reveals that the selectionof seed nodes cannot be based on a single topological characteristic, butshould involve a combination of multiple topological features to address the IMproblem.

A universal meta-heuristic framework for influence maximization in hypergraphs