论文标题
通过Riemannian预测的近端梯度方法的社区检测
Community Detection by a Riemannian Projected Proximal Gradient Method
论文作者
论文摘要
社区检测在理解和利用复杂系统的结构中起着重要作用。使用模块化最大化或其他技术开发了许多算法供社区检测。在本文中，我们将社区检测问题提出为紧凑型齿状歧管上的非平滑优化问题。提出了一种Riemannian投射的近端梯度方法，并用于解决该问题。据我们所知，这是首次将Riemannian优化用于社区发现问题的尝试。合成基准和现实世界网络的数值实验结果表明，我们的算法有效，并且表现优于几种最先进的算法。
Community detection plays an important role in understanding and exploiting the structure of complex systems. Many algorithms have been developed for community detection using modularity maximization or other techniques. In this paper, we formulate the community detection problem as a constrained nonsmooth optimization problem on the compact Stiefel manifold. A Riemannian projected proximal gradient method is proposed and used to solve the problem. To the best of our knowledge, this is the first attempt to use Riemannian optimization for community detection problem. Numerical experimental results on synthetic benchmarks and real-world networks show that our algorithm is effective and outperforms several state-of-art algorithms.
