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arXiv 1102.3722math.SPcs.DM
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利用Dirichlet特征值的通信网络谱分析

Spectral analysis of communication networks using Dirichlet eigenvalues

Alexander Tsiatas, Iraj Saniee, Onuttom Narayan, Matthew Andrews

摘要:

计算了具有Dirichlet边界条件的图Laplacian的谱间隙,并将其应用于IP层的几个通信网络的图,这些通信网络是更大的全球IP层网络的子图.我们发现,这些网络的狄利克雷谱间隙是大大大于标准的谱间隙,很可能保持非零的无限图形的限制。我们首先证明了有限正则树的这一结果,并证明了无限树极限中的Dirichlet谱间隙收敛于无限树的谱间隙。我们还执行Dirichlet谱聚类的IP层网络,并表明,它往往会产生削减附近的网络核心,创造真正的单组分集群。这比传统的谱聚类要好得多,在传统的谱聚类中,靠近外围的几个不相交的片段容易被误导地分类为单个聚类。谱聚类通常用于识别瓶颈或拥塞;由于已知这些网络中的拥塞在核心处达到峰值,因此我们的研究结果表明Dirichlet谱聚类可能更好地找到真正的瓶颈。

英文摘要:

The spectral gap of the graph Laplacian with Dirichlet boundary conditions is computed for the graphs of several communication networks at the IP-layer, which are subgraphs of the much larger global IP-layer network. We show that the Dirichlet spectral gap of these networks is substantially larger than the standard spectral gap and is likely to remain non-zero in the infinite graph limit. We first prove this result for finite regular trees, and show that the Dirichlet spectral gap in the infinite tree limit converges to the spectral gap of the infinite tree. We also perform Dirichlet spectral clustering on the IP-layer networks and show that it often yields cuts near the network core that create genuine single-component clusters. This is much better than traditional spectral clustering where several disjoint fragments near the periphery are liable to be misleadingly classified as a single cluster. Spectral clustering is often used to identify bottlenecks or congestion; since congestion in these networks is known to peak at the core, our results suggest that Dirichlet spectral clustering may be better at finding bona-fide bottlenecks.

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