Graph compression: The effect of clusters
Graph compression: The effect of clusters
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图压缩：簇的影响
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
E. Abbe
中科院分区：
文献类型：
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作者：
E. Abbe
本文研究了随机图压缩的基本限制。还讨论了图上数据的压缩。图假定有标记的顶点。最基本的例子是Erdős-Rényi模型，它对应于众所周知的压缩i.i.d位的情况。本文研究了具有簇或块模型的非齐次随机图。这些对应于Erdős-Rényi模型的混合，这些模型可能不适用于类i.i.d源的基本工具，捕获了一般网络模型的关键特征。它显示了这些模型的无损压缩的基本限制是如何随着图变得更稀疏而采取不同形式的。本文还介绍了压缩和聚类之间的联系，每个字段如何影响另一个字段，以及聚类如何帮助压缩图上的数据。
This paper investigates the fundamental limits for compressing random graphs. It also discusses the compression of data on graphs. The graphs are assumed to have labelled vertices. The most basic example is the Erdős-Rényi model, which corresponds to the well-understood case of compressing i.i.d. bits. This paper investigates inhomogeneous random graphs that have clusters, or equivalently, block models. These correspond to mixtures of Erdős-Rényi models for which basic tools for i.i.d.-like sources may not apply, capturing a key feature of general network models. It is shown how the fundamental limit of lossless compression for such models takes different forms as the graph gets sparser. The paper also introduces connections between compression and clustering, how each field can impact the other, and how clustering can help for the compression of data on graphs.
DOI：
10.1103/physreve.71.046117
发表时间：
2004-11
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
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作者：
E. Ziv;Manuel Middendorf;C. Wiggins
通讯作者：
E. Ziv;Manuel Middendorf;C. Wiggins