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首页> 外文期刊>Microprocessors and microsystems >Network embedding architecture using laplace regularization-non-negative matrix factorization for virtualization
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Network embedding architecture using laplace regularization-non-negative matrix factorization for virtualization

机译：网络嵌入架构使用LAPLACE正则化 - 虚拟化非负矩阵分解

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摘要

While modeling the applications for a problem in cloud computing, researchers and scientists frequently use graphs as abstractions. Graphs provide structural models that make it possible to analyze and understand how many separate systems act together. The omnipresence in cloud computing systems is increasing information networks. The graph embedding algorithms preserve the microscopic structure over the cloud, and many of them miss the mesoscopic structure of the networks. In this paper, asymmetric non-negative Laplace regularization for cloud platform and matrix factorization is implemented for network embedding. The proposed algorithm preserves the mesoscopic structure in cloud computing, the learned model from the Laplace, and matrix factorization. The embedded cloud network can be used for link prediction, vertex recommendation, node clustering. It is a scalable algorithm for higher proximity preserving along with community structure. The correctness and convergence are measures as performance parameters in the network. Based factorization is used for updating the rules. The experimental study shows that the proposed system is well-organized compared to the existing process in structure preservation in cloud computing.
机译：在为云计算中建模问题的应用中，研究人员和科学家经常使用图形作为抽象。图表提供了结构模型，使得可以分析和理解有多少单独的系统行动。云计算系统中的Omnipresence正在增加信息网络。嵌入算法的图形嵌入算法在云上保留了显微镜结构，其中许多人错过了网络的介观结构。在本文中，为网络嵌入实施了云平台和矩阵分解的非对称非负面LAPLACE规则。所提出的算法在云计算中保留了中学结构，来自拉普拉斯的学习模型和矩阵分解。嵌入式云网络可用于链路预测，顶点推荐，节点群集。它是一种可扩展的算法，其较高邻近保存以及社区结构。正确性和收敛性是网络中的性能参数。基于基于的因子化用于更新规则。实验研究表明，与云计算结构保存的现有过程相比，所提出的系统是良好的。

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