<p>The scalable solution to constrained combinatorial problems in high dimensions can address many challenges encountered in scientific and engineering disciplines. Inspired by the use of graph neural networks for quadratic-cost combinatorial optimization problems, Heydaribeni and colleagues proposed HypOp, which aims to efficiently solve general problems with higher-order constraints by leveraging hypergraph neural networks to extend previous algorithms to arbitrary cost functions. It incorporates a distributed training architecture to handle larger-scale tasks efficiently. Here we reproduce the primary experiments of HypOp and examine its robustness with respect to the number of graphics processing units, distributed partitioning strategies and fine-tuning methods. We also assess its transferability by applying it to the maximum clique problem and the quadratic assignment problem. The results validate the reusability of HypOp across diverse application scenarios. Furthermore, we provide guidelines offering practical insights for effectively applying it to multiple combinatorial optimization problems.</p>