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Meng Wei; Wen Huang; Kyle A. Gallivan; Paul Van Dooren

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Community Detection by a Riemannian Projected Proximal Gradient Method ? ?

机译：通过riemannian预测的近端梯度方法？

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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. Numerical experimental results on synthetic benchmarks and real-world networks show that our algorithm is effective and outperforms several state-of-art algorithms.

机译：社区检测在理解和利用复杂系统的结构方面发挥着重要作用。使用模块化最大化或其他技术开发了许多算法用于社区检测。在本文中，我们将社区检测问题制定为紧凑型Stiefel歧管上的受限非光滑优化问题。提出了riemannian预测的近端梯度方法，并用于解决问题。合成基准和现实网络上的数值实验结果表明，我们的算法是有效的，优于几种最先进的算法。

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《IFAC PapersOnLine》 |2021年第9期|共8页
作者
Meng Wei; Wen Huang; Kyle A. Gallivan; Paul Van Dooren;
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Community DetectionModularity MatrixRiemannian OptimizationProjected Proximal Gradient;

机译：社区检测常规基质矩阵修整优化产品近端梯度;

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Community Detection by a Riemannian Projected Proximal Gradient Method ? ?

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