论文标题
通过动态最佳运输公式在网络中的社区检测
Community Detection in networks by Dynamical Optimal Transport Formulation
论文作者
论文摘要
在应用程序的各个领域中检测网络中的社区很重要。尽管存在多种执行此任务的方法，但最近的努力提出了最佳运输（OT）原理，结合了Ollivier-Ricci曲率的几何概念，通过严格比较编码的信息，将节点分类为组，以将节点分类为组。我们提出了一种基于OT的方法，该方法利用了OT理论的最新进展，以允许对交通惩罚进行调整，从而强制执行不同的运输方案。结果，与基于标准的OT配方相比，我们的模型可以灵活地捕获不同的方案，从而提高恢复社区的性能准确性。我们在合成网络和真实网络中测试了算法的性能，在前一种情况下，与其他基于OT的方法相比，取得了可比或更好的性能，同时发现社区在实际数据中与Node Metadata更加一致。这进一步推动了我们对几何方法在复杂网络中捕获模式的能力的理解。
Detecting communities in networks is important in various domains of applications. While a variety of methods exists to perform this task, recent efforts propose Optimal Transport (OT) principles combined with the geometric notion of Ollivier-Ricci curvature to classify nodes into groups by rigorously comparing the information encoded into nodes' neighborhoods. We present an OT-based approach that exploits recent advances in OT theory to allow tuning for traffic penalization, which enforces different transportation schemes. As a result, our model can flexibly capture different scenarios and thus increase performance accuracy in recovering communities, compared to standard OT-based formulations. We test the performance of our algorithm in both synthetic and real networks, achieving a comparable or better performance than other OT-based methods in the former case, while finding communities more aligned with node metadata in real data. This pushes further our understanding of geometric approaches in their ability to capture patterns in complex networks.
