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Sampling is a standard approach in big-graph analytics; the goal is toefficiently estimate the graph properties by consulting a sample of the wholepopulation. A perfect sample is assumed to mirror every property of the wholepopulation. Unfortunately, such a perfect sample is hard to collect in complexpopulations such as graphs (e.g. web graphs, social networks etc), where anunderlying network connects the units of the population. Therefore, a goodsample will be representative in the sense that graph properties of interestcan be estimated with a known degree of accuracy. While previous work focusedparticularly on sampling schemes used to estimate certain graph properties(e.g. triangle count), much less is known for the case when we need to estimatevarious graph properties with the same sampling scheme. In this paper, wepropose a generic stream sampling framework for big-graph analytics, calledGraph Sample and Hold (gSH). To begin, the proposed framework samples frommassive graphs sequentially in a single pass, one edge at a time, whilemaintaining a small state. We then show how to produce unbiased estimators forvarious graph properties from the sample. Given that the graph analysisalgorithms will run on a sample instead of the whole population, the runtimecomplexity of these algorithm is kept under control. Moreover, given that theestimators of graph properties are unbiased, the approximation error is keptunder control. Finally, we show the performance of the proposed framework (gSH)on various types of graphs, such as social graphs, among others.

Graph Sample and Hold: A Framework for Big-Graph Analytics