用无监督学习优化顶点排序，加速最大团搜索
Unsupervised Ordering for Maximum Clique
- 将图的组合约束转为几何关系，学习契合团结构的顶点顺序
- 在多种图实例上显著减少计算步数，提升分支定界效率
- 适用于不同规模图，具有良好泛化能力，适合图算法研究者
我们提出一种无监督方法，通过排列框架学习最大团问题中的顶点排序。将组合约束转化为几何关系，使顶点顺序与团结构对齐。将这种面向团的排序集成到分支定界搜索中，提升了搜索效率并减少了计算步骤。实验表明，无监督学习顶点排序可有效增强多种图实例的搜索效率，并进一步研究了其在不同图规模间的泛化能力。
原文摘要 · Abstract (English)
We propose an unsupervised approach for learning vertex orderings for the maximum clique problem by framing it within a permutation-based framework. We transform the combinatorial constraints into geometric relationships such that the ordering of vertices aligns with the clique structures. By integrating this clique-oriented ordering into branch-and-bound search, we improve search efficiency and reduce the number of computational steps. Our results demonstrate how unsupervised learning of vertex ordering can enhance search efficiency across diverse graph instances. We further study the generalization across different sizes.
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