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The seminal works by Karger [13], [14] have shown that one can use Uniform Random Edge (URE) sampling to generate a graph skeleton which accurately approximates all cut-values in the original graph with high probability under some specific assumptions. As such, the random subgraphs resulted from URE sampling can often be used as substitutes for the original graphs in cut/flow-related graph-optimization...
With the explosion of graph scale of social networks, it becomes increasingly impractical to study the original large graph directly. Being able to derive a representative sample of the original graph, graph sampling provides an efficient solution for social network analysis. We expect this sample could preserve some important graph properties and represent the original graph well. If one algorithm...
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