Distributed Compression of Graphical Data

In contrast to time series, graphical data is indexed by the vertices and edges of a graph. Modern applications such as internet, social networks, genomics proteomics generate data, often at large scale. The scale argues for need compress storage subsequent processing. Since this might have several components available in different locations, it also important study distributed compression data. paper, we derive rate region problem which counterpart Slepian–Wolf theorem. We characterize when statistical description can be modeled being one two types – member sequence marked sparse Erdős–Rényi ensembles or configuration model ensembles. Our results are terms generalization notion entropy introduced Bordenave Caputo local weak limits graphs. Furthermore, give result with more than sources.