We explore graph rewiring methods that optimise commute time. Recent graph rewiring approaches facilitate long-range interactions in sparse graphs, making such rewirings commute-time-optimal on average. However, when an expert prior exists on which node pairs should or should not interact, a superior rewiring would favour short commute times between these privileged node pairs. We construct two synthetic datasets with known priors reflecting realistic settings, and use these to motivate two bespoke rewiring methods that incorporate the known prior. We investigate the regimes where our rewiring improves test performance on the synthetic datasets. Finally, we perform a case study on a real-world citation graph to investigate the practical implications of our work.

翻译：本文探讨了优化通勤时间的图结构重构方法。近期的图重构方法通过在稀疏图中促进长程交互，使得此类重构在平均意义上达到通勤时间最优。然而，当存在关于哪些节点对应该或不应该交互的专家先验知识时，更优的重构应当优先考虑这些特权节点对之间的短通勤时间。我们构建了两个具有已知先验的合成数据集，这些先验反映了现实场景，并以此为基础提出了两种融合已知先验的定制化重构方法。我们研究了在合成数据集上我们的重构方法能够提升测试性能的具体机制。最后，我们通过对真实世界引文图的案例研究，探讨了本工作的实际应用价值。